{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Phase 3 Weighted Bagging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zake7\\Anaconda3\\lib\\site-packages\\gensim\\utils.py:1197: UserWarning: detected Windows; aliasing chunkize to chunkize_serial\n",
      "  warnings.warn(\"detected Windows; aliasing chunkize to chunkize_serial\")\n",
      "Using TensorFlow backend.\n",
      "C:\\Users\\zake7\\Anaconda3\\lib\\site-packages\\fuzzywuzzy\\fuzz.py:35: UserWarning: Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning\n",
      "  warnings.warn('Using slow pure-python SequenceMatcher. Install python-Levenshtein to remove this warning')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "\n",
    "import os\n",
    "import re\n",
    "import csv\n",
    "import codecs\n",
    "import gensim\n",
    "import itertools\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import operator\n",
    "import sys\n",
    "\n",
    "from nltk import ngrams\n",
    "from collections import Counter\n",
    "from string import punctuation\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "from iwillwin.trainer.supervised_trainer import KerasModelTrainer\n",
    "from iwillwin.data_utils.data_helpers import DataTransformer, DataLoader\n",
    "from iwillwin.config import dataset_config\n",
    "from iwillwin.data_utils.feature_engineering import FeatureCreator\n",
    "\n",
    "from fuzzywuzzy import fuzz\n",
    "from nltk.corpus import stopwords\n",
    "from tqdm import tqdm\n",
    "from scipy.stats import skew, kurtosis\n",
    "from scipy.spatial.distance import cosine, cityblock, jaccard, canberra, euclidean, minkowski, braycurtis\n",
    "from nltk import word_tokenize\n",
    "\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "\n",
    "import lightgbm as lgb\n",
    "from sklearn.model_selection import train_test_split\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "import os\n",
    "import re\n",
    "import csv\n",
    "import codecs\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import operator\n",
    "from os import listdir\n",
    "from os.path import isfile, join\n",
    "\n",
    "########################################\n",
    "## import packages\n",
    "########################################\n",
    "import os\n",
    "import re\n",
    "import csv\n",
    "import codecs\n",
    "import numpy as np\n",
    "np.random.seed(1337)\n",
    "\n",
    "import pandas as pd\n",
    "import operator\n",
    "import sys\n",
    "\n",
    "from string import punctuation\n",
    "from keras.preprocessing.text import Tokenizer\n",
    "from keras.preprocessing.sequence import pad_sequences\n",
    "\n",
    "from iwillwin.trainer.supervised_trainer import KerasModelTrainer\n",
    "from iwillwin.data_utils.data_helpers import DataTransformer, DataLoader\n",
    "from iwillwin.config import dataset_config\n",
    "from keras.utils import to_categorical"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from the default dictionary ...\n",
      "Loading model from cache C:\\Users\\zake7\\AppData\\Local\\Temp\\jieba.cache\n",
      "Loading model cost 0.469 seconds.\n",
      "Prefix dict has been built succesfully.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[DataHelper] Apply normalization on value-type columns\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\zake7\\Anaconda3\\lib\\site-packages\\sklearn\\utils\\validation.py:475: DataConversionWarning: Data with input dtype int64 was converted to float64 by MinMaxScaler.\n",
      "  warnings.warn(msg, DataConversionWarning)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Doing preprocessing...\n",
      "Transforming words to indices...\n",
      "Shape of data tensor: (320552, 50) (320552, 50)\n",
      "Shape of label tensor: (320552,)\n",
      "Preprocessed.\n",
      "Number of unique words 83265\n"
     ]
    }
   ],
   "source": [
    "NB_WORDS, MAX_SEQUENCE_LENGTH = 50000, 50\n",
    "data_transformer = DataTransformer(max_num_words=NB_WORDS, max_sequence_length=MAX_SEQUENCE_LENGTH, char_level=False,\n",
    "                                   normalization=True, features_processed=True)\n",
    "trains_nns, tests_nns, labels = data_transformer.prepare_data(dual=False)\n",
    "print(\"Number of unique words\", len(data_transformer.tokenizer.index_docs))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "trains_meta = trains_nns[2]\n",
    "tests_meta = tests_nns[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_df = pd.read_csv('../data/dataset/train.csv')\n",
    "test_df = pd.read_csv('../data/dataset/test.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "nan <class 'float'>\n",
      "nan <class 'float'>\n",
      "nan <class 'float'>\n",
      "nan <class 'float'>\n",
      "nan <class 'float'>\n",
      "nan <class 'float'>\n",
      "nan <class 'float'>\n",
      "nan <class 'float'>\n"
     ]
    }
   ],
   "source": [
    "rumor_words = ['辟谣', '谣言', '勿传', '假的']\n",
    "\n",
    "def is_rumor(text):\n",
    "    if type(text) != str:\n",
    "        print(text, type(text))\n",
    "        return 0\n",
    "    for rumor_word in rumor_words:\n",
    "        if rumor_word in text:\n",
    "            return 1\n",
    "    return 0\n",
    "\n",
    "def has_split_symbol(text):\n",
    "    if type(text) != str:\n",
    "        return 0\n",
    "    if '|' in text:\n",
    "        return 1\n",
    "    return 0\n",
    "\n",
    "for df in [train_df, test_df]:\n",
    "    df['has_|'] = df['title2_zh'].apply(has_split_symbol)\n",
    "    df['has_rumor_words'] = df['title2_zh'].apply(is_rumor)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "train_has_rumor = train_df.has_rumor_words.values\n",
    "test_has_rumor = test_df.has_rumor_words.values\n",
    "\n",
    "trick_trains_features = np.concatenate((trains_nns[2], train_has_rumor.reshape((-1, 1))), axis=1)\n",
    "trick_tests_features = np.concatenate((tests_nns[2], test_has_rumor.reshape((-1, 1))), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "oof_file_names = sorted([f for f in listdir('../data/oofs/') if isfile(join('../data/oofs/', f)) and f != '.gitkeep'])\n",
    "preds_file_names = [name.replace('-Train', '') for name in oof_file_names]\n",
    "\n",
    "oofs = []\n",
    "preds = []\n",
    "for name in oof_file_names:\n",
    "    oofs.append(pd.read_csv('../data/oofs/' + name))\n",
    "for name in preds_file_names:\n",
    "    preds.append(pd.read_csv('../data/output/' + name))    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 3Embedding-3LayersDenseCNN42-NoDrop-NoClassWeighted-NoEM-Train-L0.809633-NB5000.csv\n",
      "1 3Embedding-3LayersDenseRNN42-Drop01-NoMeta-NoClassWeighted-WithEM-Train-L0.816583-NB5000.csv\n",
      "2 3Embedding-ESIM-Drop01-NoMeta-NoClassWeighted-NoEM-Train-L0.833565-NB5000.csv\n",
      "3 WordSGNS-DenseCNN5Layers-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.838202-NB100000.csv\n",
      "4 WordSGNS-ESIM-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L1.104962-NB100000.csv\n",
      "5 WordTC-DenseCNN5Layers-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.8440-NB100000.csv\n",
      "6 WordTC-DenseRNN-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.854586-NB100000.csv\n",
      "7 WordTC-ESIM-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.374334-NB100000.csv\n",
      "8 WordTC-Gated4GWindows-NoMeta-3P-NoEM-NoClassWeighted-3Layers-withEM-Train-L0.836860-NB100000.csv\n"
     ]
    }
   ],
   "source": [
    "for i, name in enumerate(oof_file_names):\n",
    "    print(i, name)\n",
    "    \n",
    "trains = pd.DataFrame()\n",
    "tests = pd.DataFrame()\n",
    "\n",
    "for i in range(len(oof_file_names)):\n",
    "    for label_type in ['agreed', 'disagreed', 'unrelated']:\n",
    "        trains['oofs_{}_{}'.format(i, label_type)] = oofs[i][label_type].values\n",
    "        tests['oofs_pred{}_{}'.format(i, label_type)] = preds[i][label_type].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['3Embedding-3LayersDenseCNN42-NoDrop-NoClassWeighted-NoEM-Train-L0.809633-NB5000.csv',\n",
       " '3Embedding-3LayersDenseRNN42-Drop01-NoMeta-NoClassWeighted-WithEM-Train-L0.816583-NB5000.csv',\n",
       " '3Embedding-ESIM-Drop01-NoMeta-NoClassWeighted-NoEM-Train-L0.833565-NB5000.csv',\n",
       " 'WordSGNS-DenseCNN5Layers-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.838202-NB100000.csv',\n",
       " 'WordSGNS-ESIM-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L1.104962-NB100000.csv',\n",
       " 'WordTC-DenseCNN5Layers-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.8440-NB100000.csv',\n",
       " 'WordTC-DenseRNN-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.854586-NB100000.csv',\n",
       " 'WordTC-ESIM-NoMeta-3P-NoEM-NoClassWeighted-3Layers-Train-L0.374334-NB100000.csv',\n",
       " 'WordTC-Gated4GWindows-NoMeta-3P-NoEM-NoClassWeighted-3Layers-withEM-Train-L0.836860-NB100000.csv']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oof_file_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "unrelated = pd.DataFrame()\n",
    "agreeds = pd.DataFrame()\n",
    "disagreeds = pd.DataFrame()\n",
    "\n",
    "#check_oofs = True\n",
    "check_oofs = False\n",
    "\n",
    "\n",
    "if check_oofs:\n",
    "    for i, oof in enumerate(oofs):\n",
    "        agreeds['oofs_agreed_{}'.format(i)] = oofs[i]['agreed'].values\n",
    "        unrelated['oofs_unrelated_{}'.format(i)] = oofs[i]['unrelated'].values\n",
    "        disagreeds['oofs_disagreeds_{}'.format(i)] = oofs[i]['disagreed'].values\n",
    "else:\n",
    "    for i, oof in enumerate(oofs):\n",
    "        agreeds['oofs_agreed_{}'.format(i)] = preds[i]['agreed'].values\n",
    "        unrelated['oofs_unrelated_{}'.format(i)] = preds[i]['unrelated'].values\n",
    "        disagreeds['oofs_disagreeds_{}'.format(i)] = preds[i]['disagreed'].values  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oofs_agreed_0</th>\n",
       "      <th>oofs_agreed_1</th>\n",
       "      <th>oofs_agreed_2</th>\n",
       "      <th>oofs_agreed_3</th>\n",
       "      <th>oofs_agreed_4</th>\n",
       "      <th>oofs_agreed_5</th>\n",
       "      <th>oofs_agreed_6</th>\n",
       "      <th>oofs_agreed_7</th>\n",
       "      <th>oofs_agreed_8</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.982310</td>\n",
       "      <td>0.976632</td>\n",
       "      <td>0.959797</td>\n",
       "      <td>0.956807</td>\n",
       "      <td>0.961449</td>\n",
       "      <td>0.961597</td>\n",
       "      <td>0.959404</td>\n",
       "      <td>0.962629</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_1</th>\n",
       "      <td>0.982310</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.982479</td>\n",
       "      <td>0.958966</td>\n",
       "      <td>0.957473</td>\n",
       "      <td>0.961494</td>\n",
       "      <td>0.961780</td>\n",
       "      <td>0.961639</td>\n",
       "      <td>0.961288</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_2</th>\n",
       "      <td>0.976632</td>\n",
       "      <td>0.982479</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.955476</td>\n",
       "      <td>0.959167</td>\n",
       "      <td>0.957512</td>\n",
       "      <td>0.961050</td>\n",
       "      <td>0.963775</td>\n",
       "      <td>0.956841</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_3</th>\n",
       "      <td>0.959797</td>\n",
       "      <td>0.958966</td>\n",
       "      <td>0.955476</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.982100</td>\n",
       "      <td>0.977313</td>\n",
       "      <td>0.970090</td>\n",
       "      <td>0.972992</td>\n",
       "      <td>0.973543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_4</th>\n",
       "      <td>0.956807</td>\n",
       "      <td>0.957473</td>\n",
       "      <td>0.959167</td>\n",
       "      <td>0.982100</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.971693</td>\n",
       "      <td>0.972289</td>\n",
       "      <td>0.979278</td>\n",
       "      <td>0.968705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_5</th>\n",
       "      <td>0.961449</td>\n",
       "      <td>0.961494</td>\n",
       "      <td>0.957512</td>\n",
       "      <td>0.977313</td>\n",
       "      <td>0.971693</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.981701</td>\n",
       "      <td>0.981716</td>\n",
       "      <td>0.982047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_6</th>\n",
       "      <td>0.961597</td>\n",
       "      <td>0.961780</td>\n",
       "      <td>0.961050</td>\n",
       "      <td>0.970090</td>\n",
       "      <td>0.972289</td>\n",
       "      <td>0.981701</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.983917</td>\n",
       "      <td>0.982819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_7</th>\n",
       "      <td>0.959404</td>\n",
       "      <td>0.961639</td>\n",
       "      <td>0.963775</td>\n",
       "      <td>0.972992</td>\n",
       "      <td>0.979278</td>\n",
       "      <td>0.981716</td>\n",
       "      <td>0.983917</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_agreed_8</th>\n",
       "      <td>0.962629</td>\n",
       "      <td>0.961288</td>\n",
       "      <td>0.956841</td>\n",
       "      <td>0.973543</td>\n",
       "      <td>0.968705</td>\n",
       "      <td>0.982047</td>\n",
       "      <td>0.982819</td>\n",
       "      <td>0.978351</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               oofs_agreed_0  oofs_agreed_1  oofs_agreed_2  oofs_agreed_3  \\\n",
       "oofs_agreed_0       1.000000       0.982310       0.976632       0.959797   \n",
       "oofs_agreed_1       0.982310       1.000000       0.982479       0.958966   \n",
       "oofs_agreed_2       0.976632       0.982479       1.000000       0.955476   \n",
       "oofs_agreed_3       0.959797       0.958966       0.955476       1.000000   \n",
       "oofs_agreed_4       0.956807       0.957473       0.959167       0.982100   \n",
       "oofs_agreed_5       0.961449       0.961494       0.957512       0.977313   \n",
       "oofs_agreed_6       0.961597       0.961780       0.961050       0.970090   \n",
       "oofs_agreed_7       0.959404       0.961639       0.963775       0.972992   \n",
       "oofs_agreed_8       0.962629       0.961288       0.956841       0.973543   \n",
       "\n",
       "               oofs_agreed_4  oofs_agreed_5  oofs_agreed_6  oofs_agreed_7  \\\n",
       "oofs_agreed_0       0.956807       0.961449       0.961597       0.959404   \n",
       "oofs_agreed_1       0.957473       0.961494       0.961780       0.961639   \n",
       "oofs_agreed_2       0.959167       0.957512       0.961050       0.963775   \n",
       "oofs_agreed_3       0.982100       0.977313       0.970090       0.972992   \n",
       "oofs_agreed_4       1.000000       0.971693       0.972289       0.979278   \n",
       "oofs_agreed_5       0.971693       1.000000       0.981701       0.981716   \n",
       "oofs_agreed_6       0.972289       0.981701       1.000000       0.983917   \n",
       "oofs_agreed_7       0.979278       0.981716       0.983917       1.000000   \n",
       "oofs_agreed_8       0.968705       0.982047       0.982819       0.978351   \n",
       "\n",
       "               oofs_agreed_8  \n",
       "oofs_agreed_0       0.962629  \n",
       "oofs_agreed_1       0.961288  \n",
       "oofs_agreed_2       0.956841  \n",
       "oofs_agreed_3       0.973543  \n",
       "oofs_agreed_4       0.968705  \n",
       "oofs_agreed_5       0.982047  \n",
       "oofs_agreed_6       0.982819  \n",
       "oofs_agreed_7       0.978351  \n",
       "oofs_agreed_8       1.000000  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agreeds.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>oofs_disagreeds_0</th>\n",
       "      <th>oofs_disagreeds_1</th>\n",
       "      <th>oofs_disagreeds_2</th>\n",
       "      <th>oofs_disagreeds_3</th>\n",
       "      <th>oofs_disagreeds_4</th>\n",
       "      <th>oofs_disagreeds_5</th>\n",
       "      <th>oofs_disagreeds_6</th>\n",
       "      <th>oofs_disagreeds_7</th>\n",
       "      <th>oofs_disagreeds_8</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>oofs_disagreeds_0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.954897</td>\n",
       "      <td>0.951416</td>\n",
       "      <td>0.912412</td>\n",
       "      <td>0.913741</td>\n",
       "      <td>0.912573</td>\n",
       "      <td>0.916230</td>\n",
       "      <td>0.912694</td>\n",
       "      <td>0.908535</td>\n",
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       "    <tr>\n",
       "      <th>oofs_disagreeds_1</th>\n",
       "      <td>0.954897</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.964977</td>\n",
       "      <td>0.920113</td>\n",
       "      <td>0.918646</td>\n",
       "      <td>0.920680</td>\n",
       "      <td>0.921862</td>\n",
       "      <td>0.920311</td>\n",
       "      <td>0.911443</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_2</th>\n",
       "      <td>0.951416</td>\n",
       "      <td>0.964977</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.919030</td>\n",
       "      <td>0.925045</td>\n",
       "      <td>0.915579</td>\n",
       "      <td>0.923305</td>\n",
       "      <td>0.926555</td>\n",
       "      <td>0.913171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_3</th>\n",
       "      <td>0.912412</td>\n",
       "      <td>0.920113</td>\n",
       "      <td>0.919030</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.967334</td>\n",
       "      <td>0.956456</td>\n",
       "      <td>0.945988</td>\n",
       "      <td>0.954167</td>\n",
       "      <td>0.943892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_4</th>\n",
       "      <td>0.913741</td>\n",
       "      <td>0.918646</td>\n",
       "      <td>0.925045</td>\n",
       "      <td>0.967334</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.946770</td>\n",
       "      <td>0.944932</td>\n",
       "      <td>0.961943</td>\n",
       "      <td>0.939010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_5</th>\n",
       "      <td>0.912573</td>\n",
       "      <td>0.920680</td>\n",
       "      <td>0.915579</td>\n",
       "      <td>0.956456</td>\n",
       "      <td>0.946770</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.965450</td>\n",
       "      <td>0.960278</td>\n",
       "      <td>0.955281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_6</th>\n",
       "      <td>0.916230</td>\n",
       "      <td>0.921862</td>\n",
       "      <td>0.923305</td>\n",
       "      <td>0.945988</td>\n",
       "      <td>0.944932</td>\n",
       "      <td>0.965450</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.963620</td>\n",
       "      <td>0.958197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_7</th>\n",
       "      <td>0.912694</td>\n",
       "      <td>0.920311</td>\n",
       "      <td>0.926555</td>\n",
       "      <td>0.954167</td>\n",
       "      <td>0.961943</td>\n",
       "      <td>0.960278</td>\n",
       "      <td>0.963620</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.954127</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_disagreeds_8</th>\n",
       "      <td>0.908535</td>\n",
       "      <td>0.911443</td>\n",
       "      <td>0.913171</td>\n",
       "      <td>0.943892</td>\n",
       "      <td>0.939010</td>\n",
       "      <td>0.955281</td>\n",
       "      <td>0.958197</td>\n",
       "      <td>0.954127</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   oofs_disagreeds_0  oofs_disagreeds_1  oofs_disagreeds_2  \\\n",
       "oofs_disagreeds_0           1.000000           0.954897           0.951416   \n",
       "oofs_disagreeds_1           0.954897           1.000000           0.964977   \n",
       "oofs_disagreeds_2           0.951416           0.964977           1.000000   \n",
       "oofs_disagreeds_3           0.912412           0.920113           0.919030   \n",
       "oofs_disagreeds_4           0.913741           0.918646           0.925045   \n",
       "oofs_disagreeds_5           0.912573           0.920680           0.915579   \n",
       "oofs_disagreeds_6           0.916230           0.921862           0.923305   \n",
       "oofs_disagreeds_7           0.912694           0.920311           0.926555   \n",
       "oofs_disagreeds_8           0.908535           0.911443           0.913171   \n",
       "\n",
       "                   oofs_disagreeds_3  oofs_disagreeds_4  oofs_disagreeds_5  \\\n",
       "oofs_disagreeds_0           0.912412           0.913741           0.912573   \n",
       "oofs_disagreeds_1           0.920113           0.918646           0.920680   \n",
       "oofs_disagreeds_2           0.919030           0.925045           0.915579   \n",
       "oofs_disagreeds_3           1.000000           0.967334           0.956456   \n",
       "oofs_disagreeds_4           0.967334           1.000000           0.946770   \n",
       "oofs_disagreeds_5           0.956456           0.946770           1.000000   \n",
       "oofs_disagreeds_6           0.945988           0.944932           0.965450   \n",
       "oofs_disagreeds_7           0.954167           0.961943           0.960278   \n",
       "oofs_disagreeds_8           0.943892           0.939010           0.955281   \n",
       "\n",
       "                   oofs_disagreeds_6  oofs_disagreeds_7  oofs_disagreeds_8  \n",
       "oofs_disagreeds_0           0.916230           0.912694           0.908535  \n",
       "oofs_disagreeds_1           0.921862           0.920311           0.911443  \n",
       "oofs_disagreeds_2           0.923305           0.926555           0.913171  \n",
       "oofs_disagreeds_3           0.945988           0.954167           0.943892  \n",
       "oofs_disagreeds_4           0.944932           0.961943           0.939010  \n",
       "oofs_disagreeds_5           0.965450           0.960278           0.955281  \n",
       "oofs_disagreeds_6           1.000000           0.963620           0.958197  \n",
       "oofs_disagreeds_7           0.963620           1.000000           0.954127  \n",
       "oofs_disagreeds_8           0.958197           0.954127           1.000000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "disagreeds.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>oofs_unrelated_0</th>\n",
       "      <th>oofs_unrelated_1</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>oofs_unrelated_0</th>\n",
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       "      <td>0.972572</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_1</th>\n",
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       "      <td>1.000000</td>\n",
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       "      <td>0.955456</td>\n",
       "      <td>0.954800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_2</th>\n",
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       "      <td>0.957542</td>\n",
       "      <td>0.949502</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_3</th>\n",
       "      <td>0.952930</td>\n",
       "      <td>0.952496</td>\n",
       "      <td>0.948158</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.979002</td>\n",
       "      <td>0.973876</td>\n",
       "      <td>0.965568</td>\n",
       "      <td>0.969264</td>\n",
       "      <td>0.969451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_4</th>\n",
       "      <td>0.949506</td>\n",
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       "      <td>0.975886</td>\n",
       "      <td>0.963249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_5</th>\n",
       "      <td>0.954771</td>\n",
       "      <td>0.955304</td>\n",
       "      <td>0.950251</td>\n",
       "      <td>0.973876</td>\n",
       "      <td>0.967039</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978773</td>\n",
       "      <td>0.978637</td>\n",
       "      <td>0.978786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_6</th>\n",
       "      <td>0.955016</td>\n",
       "      <td>0.955510</td>\n",
       "      <td>0.954365</td>\n",
       "      <td>0.965568</td>\n",
       "      <td>0.967506</td>\n",
       "      <td>0.978773</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.981089</td>\n",
       "      <td>0.979624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_7</th>\n",
       "      <td>0.952586</td>\n",
       "      <td>0.955456</td>\n",
       "      <td>0.957542</td>\n",
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       "      <td>0.975886</td>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.974969</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oofs_unrelated_8</th>\n",
       "      <td>0.955786</td>\n",
       "      <td>0.954800</td>\n",
       "      <td>0.949502</td>\n",
       "      <td>0.969451</td>\n",
       "      <td>0.963249</td>\n",
       "      <td>0.978786</td>\n",
       "      <td>0.979624</td>\n",
       "      <td>0.974969</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  oofs_unrelated_0  oofs_unrelated_1  oofs_unrelated_2  \\\n",
       "oofs_unrelated_0          1.000000          0.978855          0.972572   \n",
       "oofs_unrelated_1          0.978855          1.000000          0.979417   \n",
       "oofs_unrelated_2          0.972572          0.979417          1.000000   \n",
       "oofs_unrelated_3          0.952930          0.952496          0.948158   \n",
       "oofs_unrelated_4          0.949506          0.950368          0.952290   \n",
       "oofs_unrelated_5          0.954771          0.955304          0.950251   \n",
       "oofs_unrelated_6          0.955016          0.955510          0.954365   \n",
       "oofs_unrelated_7          0.952586          0.955456          0.957542   \n",
       "oofs_unrelated_8          0.955786          0.954800          0.949502   \n",
       "\n",
       "                  oofs_unrelated_3  oofs_unrelated_4  oofs_unrelated_5  \\\n",
       "oofs_unrelated_0          0.952930          0.949506          0.954771   \n",
       "oofs_unrelated_1          0.952496          0.950368          0.955304   \n",
       "oofs_unrelated_2          0.948158          0.952290          0.950251   \n",
       "oofs_unrelated_3          1.000000          0.979002          0.973876   \n",
       "oofs_unrelated_4          0.979002          1.000000          0.967039   \n",
       "oofs_unrelated_5          0.973876          0.967039          1.000000   \n",
       "oofs_unrelated_6          0.965568          0.967506          0.978773   \n",
       "oofs_unrelated_7          0.969264          0.975886          0.978637   \n",
       "oofs_unrelated_8          0.969451          0.963249          0.978786   \n",
       "\n",
       "                  oofs_unrelated_6  oofs_unrelated_7  oofs_unrelated_8  \n",
       "oofs_unrelated_0          0.955016          0.952586          0.955786  \n",
       "oofs_unrelated_1          0.955510          0.955456          0.954800  \n",
       "oofs_unrelated_2          0.954365          0.957542          0.949502  \n",
       "oofs_unrelated_3          0.965568          0.969264          0.969451  \n",
       "oofs_unrelated_4          0.967506          0.975886          0.963249  \n",
       "oofs_unrelated_5          0.978773          0.978637          0.978786  \n",
       "oofs_unrelated_6          1.000000          0.981089          0.979624  \n",
       "oofs_unrelated_7          0.981089          1.000000          0.974969  \n",
       "oofs_unrelated_8          0.979624          0.974969          1.000000  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "unrelated.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Prepare Different Inputs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Only use oofs\n",
    "ensemble_trains = trains.values\n",
    "ensemble_tests = tests.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use oof and meta features\n",
    "#ensemble_trains = np.concatenate((trains.values, trains_meta), axis=1)\n",
    "#ensemble_tests = np.concatenate((tests.values, tests_meta), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# use oofs and meta-features\n",
    "#ensemble_trains = np.concatenate((trains.values, trick_trains_features), axis=1)\n",
    "#ensemble_tests = np.concatenate((tests.values, trick_tests_features), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#ensemble_trains = trick_trains_features\n",
    "#ensemble_tests = trick_tests_features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LGBM"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def fit_every_feature_model(feature_data, label, feature_test_data, fold_count=3, predict=True):\n",
    "    predictions = np.zeros(shape=[len(feature_test_data), 3])\n",
    "    fold_size = len(feature_data) // fold_count\n",
    "    oofs = []\n",
    "    \n",
    "    log_loss = 0\n",
    "    for fold_id in range(fold_count):\n",
    "        print(\"Fold : \", fold_id)\n",
    "        fold_start = fold_size * fold_id\n",
    "        fold_end = fold_start + fold_size\n",
    "        if fold_id == fold_count - 1:\n",
    "            fold_end = len(feature_data)\n",
    "                \n",
    "        train_x = np.concatenate([feature_data[:fold_start], feature_data[fold_end:]])\n",
    "        train_y = np.concatenate([label[:fold_start], label[fold_end:]])\n",
    "\n",
    "        val_x = feature_data[fold_start:fold_end]\n",
    "        val_y = label[fold_start:fold_end]\n",
    "        \n",
    "        lgb_train = lgb.Dataset(train_x, train_y)\n",
    "        lgb_val = lgb.Dataset(val_x, val_y)\n",
    "        \n",
    "        lgb_params = {\n",
    "            'boosting_type' : 'gbdt',\n",
    "            'objective' : 'multiclass',\n",
    "            'num_class':3,\n",
    "            'metric' : {'multi_logloss',},\n",
    "            'learning_rate' : 0.01,\n",
    "            'feature_fraction' : 0.8,\n",
    "            'bagging_fraction': 0.9,\n",
    "            'bagging_freq': 1,\n",
    "            'num_leaves' : 4,\n",
    "            'max_depth': 16,\n",
    "            'random_state': 42,\n",
    "            'nthread': 8,\n",
    "        }\n",
    "        \n",
    "        lgbm_model = lgb.train(lgb_params, lgb_train, num_boost_round=100000, valid_sets=[lgb_train, lgb_val],\n",
    "                        early_stopping_rounds=1000, verbose_eval=100)\n",
    "        \n",
    "        lgb.plot_importance(lgbm_model)\n",
    "        plt.show()\n",
    "        if predict:\n",
    "            prediction = lgbm_model.predict(feature_test_data, num_iteration=lgbm_model.best_iteration)\n",
    "            oof_prediction = lgbm_model.predict(val_x, num_iteration=lgbm_model.best_iteration)\n",
    "            score = metrics.log_loss(val_y, oof_prediction)\n",
    "            print(\"Fold\", fold_id, \"log loss\", score, \"in\", lgbm_model.best_iteration)\n",
    "            log_loss += score\n",
    "            oofs.append(oof_prediction)\n",
    "            predictions += prediction\n",
    "            del lgbm_model\n",
    "    predictions /= fold_count   \n",
    "    print(\"Training  Finish\")\n",
    "\n",
    "    return predictions, log_loss / fold_count, oofs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold :  0\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.532487\tvalid_1's multi_logloss: 0.53514\n",
      "[200]\ttraining's multi_logloss: 0.364809\tvalid_1's multi_logloss: 0.369045\n",
      "[300]\ttraining's multi_logloss: 0.304926\tvalid_1's multi_logloss: 0.309917\n",
      "[400]\ttraining's multi_logloss: 0.280961\tvalid_1's multi_logloss: 0.286353\n",
      "[500]\ttraining's multi_logloss: 0.270438\tvalid_1's multi_logloss: 0.276165\n",
      "[600]\ttraining's multi_logloss: 0.265368\tvalid_1's multi_logloss: 0.271314\n",
      "[700]\ttraining's multi_logloss: 0.262686\tvalid_1's multi_logloss: 0.268893\n",
      "[800]\ttraining's multi_logloss: 0.261131\tvalid_1's multi_logloss: 0.267588\n",
      "[900]\ttraining's multi_logloss: 0.26017\tvalid_1's multi_logloss: 0.266855\n",
      "[1000]\ttraining's multi_logloss: 0.259533\tvalid_1's multi_logloss: 0.266442\n",
      "[1100]\ttraining's multi_logloss: 0.25909\tvalid_1's multi_logloss: 0.2662\n",
      "[1200]\ttraining's multi_logloss: 0.258771\tvalid_1's multi_logloss: 0.266068\n",
      "[1300]\ttraining's multi_logloss: 0.258524\tvalid_1's multi_logloss: 0.265985\n",
      "[1400]\ttraining's multi_logloss: 0.258326\tvalid_1's multi_logloss: 0.265952\n",
      "[1500]\ttraining's multi_logloss: 0.258145\tvalid_1's multi_logloss: 0.265937\n",
      "[1600]\ttraining's multi_logloss: 0.257979\tvalid_1's multi_logloss: 0.265923\n",
      "[1700]\ttraining's multi_logloss: 0.257817\tvalid_1's multi_logloss: 0.265925\n",
      "[1800]\ttraining's multi_logloss: 0.257658\tvalid_1's multi_logloss: 0.265943\n",
      "[1900]\ttraining's multi_logloss: 0.257501\tvalid_1's multi_logloss: 0.265954\n",
      "[2000]\ttraining's multi_logloss: 0.257354\tvalid_1's multi_logloss: 0.265977\n",
      "[2100]\ttraining's multi_logloss: 0.257204\tvalid_1's multi_logloss: 0.266001\n",
      "[2200]\ttraining's multi_logloss: 0.257061\tvalid_1's multi_logloss: 0.266038\n",
      "[2300]\ttraining's multi_logloss: 0.256919\tvalid_1's multi_logloss: 0.266074\n",
      "[2400]\ttraining's multi_logloss: 0.256788\tvalid_1's multi_logloss: 0.266098\n",
      "[2500]\ttraining's multi_logloss: 0.256653\tvalid_1's multi_logloss: 0.266117\n",
      "[2600]\ttraining's multi_logloss: 0.25652\tvalid_1's multi_logloss: 0.266142\n",
      "Early stopping, best iteration is:\n",
      "[1687]\ttraining's multi_logloss: 0.257839\tvalid_1's multi_logloss: 0.265917\n"
     ]
    },
    {
     "data": {
      "image/png": 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aBg0aMGLECMaOHcvhw4d5+eWXOXjwILt37+bDDz+kbt26LFu2LNjhG2WMQNeP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\nfNJyHMdxcob/B4rkdmbF+uijAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9702b1e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0 log loss 0.2659167615109141 in 1687\n",
      "Fold :  1\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.534817\tvalid_1's multi_logloss: 0.514361\n",
      "[200]\ttraining's multi_logloss: 0.367754\tvalid_1's multi_logloss: 0.341498\n",
      "[300]\ttraining's multi_logloss: 0.30806\tvalid_1's multi_logloss: 0.28079\n",
      "[400]\ttraining's multi_logloss: 0.284181\tvalid_1's multi_logloss: 0.256875\n",
      "[500]\ttraining's multi_logloss: 0.273674\tvalid_1's multi_logloss: 0.246564\n",
      "[600]\ttraining's multi_logloss: 0.268591\tvalid_1's multi_logloss: 0.241793\n",
      "[700]\ttraining's multi_logloss: 0.265899\tvalid_1's multi_logloss: 0.239402\n",
      "[800]\ttraining's multi_logloss: 0.264345\tvalid_1's multi_logloss: 0.238145\n",
      "[900]\ttraining's multi_logloss: 0.26337\tvalid_1's multi_logloss: 0.237445\n",
      "[1000]\ttraining's multi_logloss: 0.262728\tvalid_1's multi_logloss: 0.237037\n",
      "[1100]\ttraining's multi_logloss: 0.262286\tvalid_1's multi_logloss: 0.236787\n",
      "[1200]\ttraining's multi_logloss: 0.261968\tvalid_1's multi_logloss: 0.236627\n",
      "[1300]\ttraining's multi_logloss: 0.261717\tvalid_1's multi_logloss: 0.236518\n",
      "[1400]\ttraining's multi_logloss: 0.261515\tvalid_1's multi_logloss: 0.236443\n",
      "[1500]\ttraining's multi_logloss: 0.261324\tvalid_1's multi_logloss: 0.236396\n",
      "[1600]\ttraining's multi_logloss: 0.261149\tvalid_1's multi_logloss: 0.236351\n",
      "[1700]\ttraining's multi_logloss: 0.260983\tvalid_1's multi_logloss: 0.236319\n",
      "[1800]\ttraining's multi_logloss: 0.260819\tvalid_1's multi_logloss: 0.236297\n",
      "[1900]\ttraining's multi_logloss: 0.260653\tvalid_1's multi_logloss: 0.23629\n",
      "[2000]\ttraining's multi_logloss: 0.260499\tvalid_1's multi_logloss: 0.236287\n",
      "[2100]\ttraining's multi_logloss: 0.260341\tvalid_1's multi_logloss: 0.236281\n",
      "[2200]\ttraining's multi_logloss: 0.260191\tvalid_1's multi_logloss: 0.236288\n",
      "[2300]\ttraining's multi_logloss: 0.26004\tvalid_1's multi_logloss: 0.236279\n",
      "[2400]\ttraining's multi_logloss: 0.259893\tvalid_1's multi_logloss: 0.23627\n",
      "[2500]\ttraining's multi_logloss: 0.259755\tvalid_1's multi_logloss: 0.236273\n",
      "[2600]\ttraining's multi_logloss: 0.259617\tvalid_1's multi_logloss: 0.236267\n",
      "[2700]\ttraining's multi_logloss: 0.259481\tvalid_1's multi_logloss: 0.236263\n",
      "[2800]\ttraining's multi_logloss: 0.259343\tvalid_1's multi_logloss: 0.236257\n",
      "[2900]\ttraining's multi_logloss: 0.259212\tvalid_1's multi_logloss: 0.236262\n",
      "[3000]\ttraining's multi_logloss: 0.259083\tvalid_1's multi_logloss: 0.236261\n",
      "[3100]\ttraining's multi_logloss: 0.258953\tvalid_1's multi_logloss: 0.236259\n",
      "[3200]\ttraining's multi_logloss: 0.258824\tvalid_1's multi_logloss: 0.236258\n",
      "[3300]\ttraining's multi_logloss: 0.258704\tvalid_1's multi_logloss: 0.236261\n",
      "[3400]\ttraining's multi_logloss: 0.25858\tvalid_1's multi_logloss: 0.236271\n",
      "[3500]\ttraining's multi_logloss: 0.258458\tvalid_1's multi_logloss: 0.236286\n",
      "[3600]\ttraining's multi_logloss: 0.258334\tvalid_1's multi_logloss: 0.236295\n",
      "[3700]\ttraining's multi_logloss: 0.258212\tvalid_1's multi_logloss: 0.236305\n",
      "[3800]\ttraining's multi_logloss: 0.258096\tvalid_1's multi_logloss: 0.236319\n",
      "[3900]\ttraining's multi_logloss: 0.257985\tvalid_1's multi_logloss: 0.236335\n",
      "[4000]\ttraining's multi_logloss: 0.25787\tvalid_1's multi_logloss: 0.236342\n",
      "[4100]\ttraining's multi_logloss: 0.257753\tvalid_1's multi_logloss: 0.236348\n",
      "Early stopping, best iteration is:\n",
      "[3162]\ttraining's multi_logloss: 0.25887\tvalid_1's multi_logloss: 0.236255\n"
     ]
    },
    {
     "data": {
      "image/png": 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l88C0D3hMREKAGhReaLuJDjQYLgMGDhzIsmXLSE1NpVSpUhw/fpzKlStTokQJ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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a975c1b6a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 1 log loss 0.23625467509002543 in 3162\n",
      "Fold :  2\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.532725\tvalid_1's multi_logloss: 0.533577\n",
      "[200]\ttraining's multi_logloss: 0.365179\tvalid_1's multi_logloss: 0.366053\n",
      "[300]\ttraining's multi_logloss: 0.305364\tvalid_1's multi_logloss: 0.305964\n",
      "[400]\ttraining's multi_logloss: 0.281461\tvalid_1's multi_logloss: 0.28197\n",
      "[500]\ttraining's multi_logloss: 0.270989\tvalid_1's multi_logloss: 0.271342\n",
      "[600]\ttraining's multi_logloss: 0.265951\tvalid_1's multi_logloss: 0.266222\n",
      "[700]\ttraining's multi_logloss: 0.263275\tvalid_1's multi_logloss: 0.26358\n",
      "[800]\ttraining's multi_logloss: 0.261728\tvalid_1's multi_logloss: 0.26216\n",
      "[900]\ttraining's multi_logloss: 0.260768\tvalid_1's multi_logloss: 0.261349\n",
      "[1000]\ttraining's multi_logloss: 0.260129\tvalid_1's multi_logloss: 0.260865\n",
      "[1100]\ttraining's multi_logloss: 0.259689\tvalid_1's multi_logloss: 0.260556\n",
      "[1200]\ttraining's multi_logloss: 0.259363\tvalid_1's multi_logloss: 0.260382\n",
      "[1300]\ttraining's multi_logloss: 0.25911\tvalid_1's multi_logloss: 0.26027\n",
      "[1400]\ttraining's multi_logloss: 0.2589\tvalid_1's multi_logloss: 0.260214\n",
      "[1500]\ttraining's multi_logloss: 0.258706\tvalid_1's multi_logloss: 0.260179\n",
      "[1600]\ttraining's multi_logloss: 0.258525\tvalid_1's multi_logloss: 0.26016\n",
      "[1700]\ttraining's multi_logloss: 0.25836\tvalid_1's multi_logloss: 0.260136\n",
      "[1800]\ttraining's multi_logloss: 0.258193\tvalid_1's multi_logloss: 0.26013\n",
      "[1900]\ttraining's multi_logloss: 0.258028\tvalid_1's multi_logloss: 0.260114\n",
      "[2000]\ttraining's multi_logloss: 0.257875\tvalid_1's multi_logloss: 0.260113\n",
      "[2100]\ttraining's multi_logloss: 0.25772\tvalid_1's multi_logloss: 0.260108\n",
      "[2200]\ttraining's multi_logloss: 0.257566\tvalid_1's multi_logloss: 0.260103\n",
      "[2300]\ttraining's multi_logloss: 0.257421\tvalid_1's multi_logloss: 0.260107\n",
      "[2400]\ttraining's multi_logloss: 0.257283\tvalid_1's multi_logloss: 0.260103\n",
      "[2500]\ttraining's multi_logloss: 0.257143\tvalid_1's multi_logloss: 0.260112\n",
      "[2600]\ttraining's multi_logloss: 0.257012\tvalid_1's multi_logloss: 0.260121\n",
      "[2700]\ttraining's multi_logloss: 0.256875\tvalid_1's multi_logloss: 0.260129\n",
      "[2800]\ttraining's multi_logloss: 0.256736\tvalid_1's multi_logloss: 0.260136\n",
      "[2900]\ttraining's multi_logloss: 0.256604\tvalid_1's multi_logloss: 0.260143\n",
      "[3000]\ttraining's multi_logloss: 0.256472\tvalid_1's multi_logloss: 0.260162\n",
      "[3100]\ttraining's multi_logloss: 0.256344\tvalid_1's multi_logloss: 0.260174\n",
      "Early stopping, best iteration is:\n",
      "[2179]\ttraining's multi_logloss: 0.2576\tvalid_1's multi_logloss: 0.260096\n"
     ]
    },
    {
     "data": {
      "image/png": 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evMuP3V966SXKlSuXrV9wcDCPPvoot99+OwMGDHDsX7ly5RXnGDRoEIMGDXJ5/l69etGr\nV69Cysa/KXJrEqA2rvVb3wFN8YJ+y5U1iaoeBV4HfhKRc8CLFGwpKIPB4Ofs2rWL5s2bU6ZMGYKD\ng2nRogVr1651HFdV5s+fT9euXR37lixZQq1atWjYsKE3Qg5YvGJNoqp7gM452kcDx9XWamWhqvsB\nh34L6ObqfJpDv+XqmKpOB6Y77W9vn/scrq1JVgLPAo1U9bSIVM4nbYPBEIDExMQwZMgQTp06RUhI\nCF988UU2n6i1a9dSpUoVateuDVg6q9GjR7NixQqX5oiGgmOsScjTmuRp4D1VPW23O5GzQU6MTsv3\nCJRcTB7eYX9CO+rXr8+gQYOIj48nLCyMxo0bc+rUKUebOXPmZLvKGjZsGP379y9Um3mDhVyuXSjk\ngUVeAG5U1f459j8K9AEewFp8diNwO5Zu6jNVjcln0lKgrap+KSKLgVCgHdAA66ou1u4/FKvy7zyQ\nDNytqodyiXW9ff5GWLceAZ4E3sASPN+FVYo/XFWvkKI767QqVap06/z586/24/I5UlNTA+Y/XKDk\nYvLwHaZMmULZsmXp0qULmZmZPPbYY0yePJlKlSoB8MILLzhEw6mpqQQFBdGzZ086duzozbBzxRe+\nk5YtW25W1dvya+eNQgxHaTtwXESyStu3utk/Z2n7eVXNEBGXpe0AIpJV2u5y0lLV2+12qaoam7Xf\nrjqsDcQBkcBaEYlR1ZQc/T8APgCoW7euxsXFuZmK75KYmEgg5AGBk4vJw7ucOHGCypUrc/DgQTZv\n3szYsWOJi4vjq6++olGjRjz22GOOtlu3Xv51Nnz4cMLCwrIVYvga/vSdeHLS8pfS9rw4DKyz9V77\nRCQZaxLbWICxDAaDH/Poo49y6tQpSpYsyXvvvUeJEiUAmDt3brZbgwbP4slJy19K2/NiCbYRpIhE\nAHWwVtQwGAwBQk791Ysvvshrr73G0qVLCQoKonLlykyfPp21a9eSmJhIhw4deOmll0hNTeWpp55i\n+vTpeY5fGHYchssYaxLytCb5Gjhl315cDQxU1VO5jWMwGPwLZ/3Vli1b+Oyzz9i7dy8DBw5k69at\nJCUl0b59e0aMGOHoc88995CUlMTUqVMZOnSoF6Mvnnj0mZatdeqcc7+IvAXUwHqWNUxEumNV/l1R\n2m6Xwn+muViT5DhfnqXtecT5MnBFdaEdxz9E5GaglqrOzTNhg8HgVzjrrwBatGjB4sWLefnly78O\nnG1CDN6nyNcedNJvJarls9UAq2y9SlHH4g4i8giQmm9Dg8Hgd8TExLBmzRpOnTpFeno6X3zxBYcO\nWfVaQ4YMoUaNGnzyySfZrrR++OEHGjduzKBBg9ixY4e3Qi+2eKzkPdcTivwZq3T83hz7BUtvlU2/\n5XSllV8pfGFbkyAiYViVis8A83MKn53aOVuT3Dp0wpQCfTa+RJUQOH7W21EUDoGSi8mjcGlU3Vpy\n6fPPP2fp0qWEhIQQFRVF6dKlef755x3tPvnkEy5cuEDPnj1JS0sjKCiIkJAQEhMTmTp1KrNmzfJW\nCoWGP5W8G2uSvK1JxmM9l3PEkN/LWJP4HoGSi8nD87zyyiv63nvvZdu3f//+bNYiWaxevVqjoqL0\n5MmTRRWex/CF7wRjTXLN1iSxwM2quvjq0jAYDP5Elgj44MGDfPrpp3Tt2pW9ey+v6LZs2TLq1asH\nWIaOat+d2rVrF5cuXaJixYpFH3QxxliT5P4Z3AHcKiL77TaVRSRRVePciNNgMPgJOfVXFSpUoHfv\n3iQnJxMUFERUVBSTJlmr0S1cuJD333+f4OBgMjIymDt3rinSKGK8caW1CigtIk9n7cih3yohIpWw\n9FsbcvTdD8SKSJCI1MCD+i1VfV9Vq6lqNNZV4B4zYRkMvsX48eNp2LAhMTExdO3alXPnzqGqDBky\nhDp16lC/fn3eeecdAP744w8efPBBGjduTMOGDfnoo48Aa7HbnTt3smXLFlq1agVY3lbbt29n69at\nLF++nOrVqwPQt29fduzYwZYtW/jXv/7FnXfe6Z3EizFFfqWlqioiHYEJIjIYOIc1Gb0IhGHptxRb\nv2UXYmThrN/ajof1WwaDwXc5cuQI77zzDjt37iQkJITOnTszd+5cVJVDhw6xe/dugoKCHLf/3nvv\nPRo0aMDy5cs5efIkdevWpVu3bpQqVcrLmRiuBo9OWiJyAzAB69nUeezJSXO3JmmjRWxNIiJlRORz\nrNXgM4HlqprTofg2oKGI3Kaqm/LL22AwFA0XL17k7NmzlCxZkvT0dKpVq8arr77K7NmzCQqybiRV\nrmw5CokIZ86cQVVJTU0lPDyc4GBvPCExXAte8dPCWjndlxinqqtFpBSwUkTaqOqXACJyPVbF43p3\nBjLWJL5HoORi8rjM/oR2VK9enQEDBlCzZk1CQkK4//77uf/+++natSvz5s1j8eLFVKpUiXfeeYfa\ntWvTt29fHnroIapVq8aZM2eYN2+eY2Iz+A/F3k9LVdNFJEFEStu7qgOTRaSdWvqtN+zxc12iOYdO\ni6GNLrr9IfkqVUKsXy6BQKDkYvK4TGJiImfOnGHGjBnMmjWLsLAwhg8fzpAhQ0hPT+fIkSOMGzeO\nNWvW8Oijj/LOO+/w3XffERERwezZszl69Ci9e/dm6tSphIaGFiiG1NRUEhMTrykPX8GvcnGnLr4g\nL/xIj+V0nvJ2v1r2dhNgkf0+EWsiNTotPyNQcjF5ZGf+/Pnaq1cvx/aMGTP0b3/7m9atW1f37dun\nqqqXLl3SsmXLqqpq27Ztdc2aNY72LVu21PXr1xf4/IHyfaj6Ri74sE7Lp/RYWdhl8XOAd1T1V3uF\n+fHAS1cRm8FgKCJq1qzJunXrSE9PR1VZuXIl9evX5+GHH2bVqlUAfPfdd9SpU8fRfuXKlQAcP36c\n5ORkatWq5bX4DQXD+Gld5gNgr6pOsLevxyoASbR1GDcAy0TkITXFGAaD17n99tvp1KkTTZs2JTg4\nmCZNmvDMM89w9uxZunXrxvjx4wkLC2Pq1KkAvPbaa/To0YNGjRqhqowePZqIiAgvZ2G4WoyflhXX\nSKxbib2z9qnlehzh1CYRGGAmLIPBu4wfP56pU6ciIjRq1IikpCT69OnDd999x+233w7A9OnTiY11\nmJCzceNGmjdvzrx58+jUydXf0gZ/wWOTlqp/6LFEJBIYAuwGfrSvqt5V1ameOqfBYCgYuWmzAMaO\nHetyQsrMzGTQoEG0bt26qMM1eIBi76elqofJ5ZaliPwTeAqo4Hxeg8HgPVxps/Ji4sSJPProo2zc\nuLGIIjR4kiJX1vmZfms58C6wN7+GWRidlu8RKLkU9zzy0mbNnj2bIUOGMGLECFq1akVCQgKlS5fm\nyJEjLF68mFWrVplJK0Awflp5+Gk5xZaa15WW8dPybQIll+KeR6Pq5Thz5gzDhg1j6NChDm1WixYt\naNq0KeHh4WRkZPDWW29RrVo1unfvzvDhw+ncuTMNGjQgISGBO+64gxYtWhRKHr7gQVVY+EIuxk+r\ncPVbqe7mZ3Ravkeg5GLyyF2blXP8du3aqapqdHS0RkVFaVRUlIaGhmqlSpV08eLFBT5/zvMECr6Q\nC27qtHxp4S2Hfgs4LiJZ+q2tbvbPqd86r6oZIuJSvwUgIln6rUOFEL/BYPAwztqskJAQVq5cyW23\n3caxY8eoWrUqqsqSJUuIibGWMN23b5+jb48ePWjfvj0PP/ywt8I3FALGT8s7n4HBYCgAuWmz2rRp\nw8mTJ1FVYmNjHf5XhsDD+GkZDAafw5VPVrdu3ahbty6LFi3izjvv5KeffuLjjz8mPT2dcuXKISKU\nKVOGwYMHu3w+M336dKPRCgA8OmmJyA0iMldEfhGRnSLyBVAb6AjE2/t3AMOxlnNqiqXfWoWt38ox\npLN+axyFpN8SkX+KyCG7mMN5/xcikgmEisgFEVlWGOczGAy5k6XF2rRpE9u3byczM5O5c+fSrVs3\ndu/ezbZt2zh79qxjpYs333yT2NhYtm7dysyZM+nXr5+XMzB4Eq9Yk2juflrHtYj9tOxzn8N1aft8\n4Fe1qxUNBkPR4EqLdf/99zuO/+lPf+Lw4cMA7Ny5k1deeQWAevXqsX//fo4fP06VKlW8ErvBsxR7\naxI7rnX2ea45aaPT8j0CJZfikEdeWqwsMjIy+Pjjj3n77bcBaNy4MZ9++il33303GzZs4MCBAxw+\nfNhMWgGKJyetGGCzi/2PALFYpegRwEYRWXMV44YCiao6SEQWAyOBeKABMAPIuoUXi2Utch5IFpGJ\nquqySlBE1mPptkJEJMne/aT981ERuRdL+Nzf1RjOOq1KlSox/4GC+fP4EqmpqUwPgDwgcHIpDnnk\n5ZMVHx8PwLhx46hVqxaZmZkkJiZy11138e6773LzzTdTq1Ytbr75Zn766SfOnDnj8Tz8xoMqH/wp\nF29Uzvlcabuq3m63S1VVxyqbInLUjvW8iPTBmhT/7KL/B1irxFO3bl2Ni4tzMxXfJTExkUDIAwIn\nl+KSx4IFC2jSpImjNP3o0aOsW7eOuLg4Xn/9dYKDg5k/f3421+F27doBlu70xhtvpHPnzpQtW9ar\nefgT/pSLJwsxdgC3utjvsdJ2sk/C11zarqqnVDVrnCm4zsdgMBQiuflkTZ06la+//po5c+Zkm7BS\nUlK4cOECAFOnTuXee+/1+IRl8B6enLT8vrRdRKo6bT4E7PJGHAZDoJGcnExsbKzjVbZsWSZMmMCW\nLVt48cUXOXnyJJUrV6Zhw4ZcunSJHj168Mwzz7Bp0ybCw8O5+eabGTFiBAC7du2iYcOG1KtXjy+/\n/NLxrMsQmBR7axIAERkD/AUoIyKHgal2BeILIvIQ1pXf71jLQBkMhmukbt26JCVZj48zMzOpXr06\nHTt2pFOnTowbN44WLVowbdo09u3bxxtvvMF7771H9+7d+eijjzhx4gRt2rTh1VdfBeCOO+5g7163\n17Q2+Dke1Wmp6lFV7ayqN6lqQ1Vtp6p7gbewJqMyWNYkXwCl1MmaRFW7qWpDYBAQoblYk6jqOKdt\nR2m7c5m6qrbP6p9LnC+raqSqBtk/h9uHFmLdggzFmkCTr/lDMRgM2Vi5ciU33XQTUVFRJCcnc++9\n1lra8fHxLFq0CLDK2lu1agVA5cqVKV++PJs2GT/W4kiRr4jhpN9KtCezBlhl675Yn/o+VlVgbfv1\ngHfDMRgCj7lz59K1a1cAYmJiWLbMKgBesGABhw5ZtVONGzdm6dKlXLx4kX379rF582bHMUPxwliT\n5GJNYj/PWq2q9eztrlgryj/roq2xJvFhAiWXQMnjxnIlHMssZWRk0KlTJz766CPCw8M5ePAgEydO\n5I8//uCuu+7i008/ZenSpWRmZjJp0iR++uknqlSpQmZmJu3bt+fuu+/2Wh6+YOdRWPhCLsaa5Bqt\nSYDbgG+dtu/BmjyNNYmfESi5BGIeS5Ys0fj4eJftkpOTtVmzZi6P3XHHHbpjxw5PhOc2gfJ9qPpG\nLrhpTeKNBXNzw6HfUtXjWGsRNruK/jn1W9+paob9Ptqp3UpV/UNVzwFZ+i1XuCrNL9rLUoMhwJkz\nZ47j1iDAiRMnALh06RIjR46kT58+AKSnp5OWlgbAihUrCA4OpkGDBkUfsMHreGPS8hf91mEg0mk7\nEjjqRowGg8EN0tPTWbFiBY888ohj35w5c6hTpw716tWjWrVq9OzZE7Ams6ZNm1K/fn1Gjx7Nxx9/\n7K2wDV7GWJPkgqoeA86ISHP7edtTwFJPnc9g8Fdy01xlMW7cOESE3377DbCenzz44IPccccd3HDD\nDXz66aeOtv369WPPnj3s2bOHhIQEx3qg0dHRJCcns2vXLr799luionK7QWIIdIp8GSdV/9FvAX/D\nWhU+BPjSfhkMBidy01wBHDp0iBUrVlCzZk1H+yVLltCgQQOWL1/OyZMnqVu3Lt26daNUqVJeid/g\nX3h00hKRG4AJWM+mzmNPTpq7NUkbLWJrEhEpIyKfY60GnwksV9XBdpMyWM/K6mGV6JtnWgZDHjhr\nrgD69+/PmDFj6NChg6ONiHDmzBlUldTUVMLDwwkONgbiBvfwip8W1orpvsQ4VV0tIqWAlSLSRlW/\nxKpg7AEMcHcgY03iewRKLr6Yx/6Edtm2nTVXy5Yto3r16jRu3Dhbm44dOzJ27FiqVavGmTNnmDdv\nXra1BA2GvCj2flqqmi4iCSKJwbLqAAAgAElEQVRS2t5VHZgsIu3U1m+JyKW8Es2h02Joo4vufUI+\nTJUQ65dkIBAoufhiHs52FhkZGSxatIj27dvz1VdfMWjQIMaOHUtiYiLnzp3j3//+N+XKlWPt2rVE\nREQwe/Zsjh49Su/evZk6dSqhof5lu+JPdh754Ve5uFMXX5AXfqLHyhFbebtfrRz7pwOd3Mnb6LR8\nj0DJxdfzcNZcbd26VStVqqRRUVEaFRWlJUqU0Bo1auixY8f09ttv1zVr1jj6tWzZUtevX++tsAuM\nr38fV4Mv5IIP67R8TY8FgIgEA3OAd1T116uIx2AwkF1z1ahRI06cOMH+/fvZv38/kZGR/Pjjj9xw\nww1UqVKFlStXAnD8+HGSk5OpVauWN0M3+BHGT+syHwB7VXVCPu0MBkMOXGmucuPJJ5/kP//5D40a\nNaJVq1aMHj2aiIiIIojSEAgYPy0rrpFYtxJf9OR5DAZ/JzdN1rBhw6hUqRL33HMPHTt2JCUlBYBP\nPvmE2NhYypcvz3333UdQUBApKSl88803bNu2je3bt/PEE094OSuDP+GxScu+GuoIxIvILyKyAxgO\nzAa2YumxVmHrsXJ0d9ZjjcODeiwRiQSGAA2AH0UkSUR628ea2f5aj2EVZ+zwVBwGgz+QpclKSkpi\n8+bNlClTho4dOxIfH8/27dvZunUrderUYdSoUQB069bN0f7jjz8mOjqam2++2ctZGPwZT4sjLtkv\nsG4LCtbK8gOBgbl1sie8QtVj5XGuwyLyJtaKFzWcx8eaXP+NdZvzFNAlt3EMhuKGsybLeYWK5s2b\ns3Dhwiva51xn0GAoCEanZbEceBfIaX/6V+C0qt4sIo8Do8ln4jI6Ld8jUHLxdh55abKcmTZtGl26\nXPnfZN68eSxdutSxnJPBUBCKlU5LRNZj+Wg586SqrrPPkzOHDli3NMFyMX5XRMSpECQrXqPT8mEC\nJRdv55GbJst5/6xZs0hJSaF69erZ9u/cuRNV5bfffvMvTVAeBEoe4Ge5uFMXX5AX/qnTSs2xvR2I\ndNr+BYjIawyj0/I9AiUXX8rDlQ/W9OnTtXnz5pqWlnZF+xdffFH/+c9/qqpv5XEtBEoeqr6RC27q\ntLyx4JdDpwUcF5EsndZWN/vn1GmdV9UMEXGp0wIQkSyd1tX6cxtPLYPBBTmfT3311VeMHj2a7777\njjJlymRre+nSJRYsWMCaNWuKOkxDAGJ0WnlzGKgBDvFxOeD3AoxjMAQMrjRZffv25cyZM8THxxMb\nG+swbwRYs2YNkZGRRkBsKBSMTitvlgHd7fedgFVOE6bBEPC40mV98MEHTJo0iTvvvJOgoCA2bdrE\nzz//zKFDhxg7diwlSpTg3//+N7feeiurVq0iLi6OdevWeTsVQ4DgsduDqv7jmyUiY4C/AGVsXdZU\ntcrpPwQ+FpGfsa6wHvdkHAaDr5GbV1Z6ejqffvopzz77bLb2ERERLF++nGrVqrF9+3Zat27NkSNH\nvBG6IUDx6DMtVT1KDt8sABF5C+u2WzNgmIh0x6r8u8I3y57MPlPVRPtYoeq07OMvA9lWgReRMsAC\nrOrD81hXWWZNQkOxJadXliuaNGnieN+wYUPOnTvH+fPnKV06Z9GuwVAwirwQw8/0W7n5bOWK0Wn5\nHoGSS1Hn4a4uKzcWLVpEkyZNzIRlKFS8UT3oTf3WL0BFrGd5pYEU4D61fbOcUdV0YLX9/oKI/AhE\nukrI6LR8m0DJpajzcEeXlZKSwubNm0lNTc3Wd9++fbz66quMGTPmCv2PX2mC8iBQ8gA/y8WduvjC\nfOGf+i2XPluuXkan5XsESi7ezMOVLktVtUWLFrpx48Zs+w4dOqS1a9fW77//3uVY5vvwPXwhF3zY\nTys3jM+WweCjuLtuYEpKCu3atWPUqFHcddddRRCZobjhjUnL3/RbxmfLUCzJKne/5ZZbWLhwIS+8\n8AITJkzg999/p3HjxgQHB7NmzRratm1L69atGTt2LPXr13fYjYiIwwzSYCgsrnrSEpEKInLLNZzT\nb/RbxmfLUJzJKnffunUr58+fJzQ0lI4dO5KQkEDXrl25ePEib775Jj179uTrr79m4MCBHDt2jMzM\nTObPn0/Lli3Ztm0blStX9nYqhgDCrUlLRBJFpKyIhGPpqz4Skf9XkBPaV0PPAK+JSIaInAM+B/6D\na5+tSCDLgMcbPlvtgTQROSsi/T11PoPBl3Eud1+6dCndu1ua++7du7NkyZIr2hsbEoOncLd6sJyq\n/s82R/xIVYeJiLtrBWbDLnmfBLypdgWhXfJ+vbr22ToM/Axe8dn6Dhigqpvcy85gCEycy92PHz9O\n1apVAahateoVt//S09P56quvePfdd4s8TkPg4+6kFSwiVbGEwkOu8Zw+Z1lyjflkw+i0fI9AyaUo\n83DWaF24cIFly5Y53IjzY/ny5dx1112Eh4d7KjxDMcbdSWsE8DXwb1XdKCK1uNIw0V1igM0u9j8C\nxGKVqEcAG0XkapaFDgUSVXWQiCwGRgLxQANgBtY6gtjnaIJVkJEsIhOxvLKu8Nmyf34kIpnAIqyJ\n9Iq1B511WpUqVWL+A6FXEbZvkpqayvQAyAMCJ5eizMNZs/P9999z4403smvXLnbt2kXZsmVZtGgR\nFStW5NSpU1x//fXZ2r/77ru0aNEiV92PX2mC8iBQ8gA/y8WduvjCfJG7Tms80Mtp+2Osq6Jo3NNp\nnQfEfj8CGGK/DwJSnPpPcer/JXB3HrFWt39ej6X7eiq//IxOy/cIlFy8lUeXLl102rRpju0BAwbo\nqFGjVFV11KhROnDgQMexlJQUrVChgqampuY6nvk+fA9fyIXC1GmJSB0RWSki2+3tW0Tk1YJMkvhR\nybuqHrF/ngFm473V5g0Gr+DKhmTw4MGsWLGC2rVrs2LFCgYPHuw4tnjxYu6//35CQ/3/ytbgm7hb\n8j4FeAXIAFDVrRR8xXO/KHkXkWARibDfl8SqItzuqfMZDL5GcnIyd955JzVq1KBFixaULVuWCRMm\nICIEBVm/OoKCgrBqqyyio6PZvXs3DRs2pEWLFt4K3RDAuDtplVHVnBNIgRZBs6+GOgLxIvKLiOwA\nhmNdybgqeXemyEresZ5xfW1XSSYBR7Amb4OhWJCl00pKSmLz5s2UKVPGodNq1aoVe/fupVWrViQk\nJADWahjPPfccy5YtY8eOHSxYsMDLGRgCEXcLMX4TkZuwreZFpBNw7BrOe8l+gXVbULCeR7kqeXdg\nT3hFVfKeJiInsdY/hCsLNQyGYkNOnVbWQ/vu3bsTFxfH6NGjmT17No888gg1a9YEMKJig0dw90rr\neWAyUE9EjmCtENEn7y6ucbImSVTVm1S1AVZ5epWCjOdhOqtqY6yKx0rAY16Ox2DwCu7otPbs2cPp\n06eJi4vj1ltvZebMmV6L1xC45HulJSJBWNqo+0QkFAiyCxMKis/ptERkPS5K3vWyZUkwUMqO6wpy\nWpNM/GRpAT4W36JKCAGRBwROLkWZR6Pq5Rzvc9qSXLx4MVt5dNb2gQMHSE5O5q233uLChQs8//zz\niAg1atTINrZflVfnQaDkAX6WizslhsAad9q5OZZfWZNg6dNOYz1zK5Fffqbk3fcIlFy8lUdOW5I6\ndero0aNHVVX16NGjmvVvftSoUTps2DBHu169eun8+fOvGM98H76HL+RCIVuTrBCRASJSQ0TCs15X\nO0Hmg09ak6hqa6zJszTw56uIx2AICHKuI/jQQw8xY8YMAGbMmEGHDh0A6NChA2vXruXixYukp6ez\nfv166tev75WYDYGLu4UYveyfzzvtU6BWAc65A+jkYr/HdFq2J1YWV2tNgqqeE5FlQAesq0GDoViQ\npdOaPHmyY9/gwYPp3LkzH374ITVr1nRUCdavX58HHniAW265haCgIHr37k1MTIy3QjcEKG5daanq\njS5eBZmwwH90WmH2eotZRpBtgd2eOp/B4EtkeWll6bRq1Kjh8NJ6/PHHOXjwINHR0SxcuJDw8HAS\nExMpV64cn3zyCaVKlaJz5868+KJx9DEUPm5daYnIU672q+pVlwepqopIR2CCiAwGzmFNRi8CYVg6\nLcXWaYlItFN3Z53Wdjyr0woFlolIaaznbKuwVqc3GAKeLI0WQGZmJtWrV8+m0Ro8eDAJCQkkJCQw\nevRoAO655x4+++wzb4ZtKAa4e3vQ+dnSdUArrAmjoDWt/qDTOi4idwHvAnFYi+92wFo412AoNrij\n0TIYigq3Ji1V/bvztoiUw1rQ9qpx0mnNUNXH7X2xWBWDewoypgcZApxQ1Tp26X++xSfGmsT3CJRc\niioPZ1sScN9L64cffqBx48ZUq1aNcePG0bBhQ4/Haih+uHullZN0oHYB+/qNTgurAKWeHeMl4DdX\nCeXUaQ1tVKAVrnyKKiHWL8lAIFByKao8nPU67mq00tLSmDVrFiEhIaxbt47WrVsza9Ysl+P7lSYo\nDwIlD/CzXNypiweWY/lRLcPSRv0KjHanr4ux/EKnBZQHDgH/D+tW6AKgSn75GZ2W7xEouXgjD3c1\nWjmJiorSkydPujxmvg/fwxdyoZB1WuOAt+zXKOBeVR3kZl938TWdVjAQiWV82RT4AetzMBiKDe5q\ntP773/9m/bHHhg0buHTpEhUrViz6gA0Bj7u3B9vmnKREZHQBJy5/0WmdwroNutjeXgD81Y0YDYaA\n4Go0WgsXLuT9998nODiYkJAQ5s6dm82yxGAoLNy90op3sa9NAc/pFzotewJcjlU5CFbF5E5Pnc9g\n8BVSUlLo1KkTTZs2pXLlyuzcuZMtW7Zwxx13EBcXR5kyZdi8eTMrV64kPNyqTXrooYc4cOAATz75\nJOvWrePOO+/0chaGQCXPSUtE/iYi24C6IrLV6bUPy/vqqrEnA3/w0wIYBAy3PbWeBF7y8PkMBq/T\nr18/HnjgAXbv3s2WLVuoX78+vXv3JiEhgW3bttGxY0fGjh2brU///v1p06agf8caDO6T3+3B2cCX\nWM+xBjvtP6Oqv1/DeX1epyUi1wNZS2pfwiro+AeWCNpgCEj+97//sWbNGqZPnw5AqVKlKFWqFMnJ\nydx7770AxMfH07p1a9544w0AlixZQq1atQgNDfVW2IZiRJ6Tlqr+AfwBdAUQkcpYz5HCRCRMVQ9e\n7Qn9Raellv1KbNa2iGwGPs2vn9Fp+R6Bkosn88jSZv36669UqlSJnj17smXLFm699VbefvttYmJi\nWLZsGR06dGDBggUcOnQIgLS0NEaPHs2KFSsYN87UKRk8j1yuXcijkciDWKXf1YATWBV3u1T1qtWD\nIvJnYLiq3ptjv2DpqrLptOxlnD5T1Rhv+WmJSG2sW5Y11cUHlkOndevQCVOu9mPxOaqEwPGz3o6i\ncAiUXDyZR5Z/VnJyMs899xwTJ06kQYMGTJw4kdDQUO677z4mTpzIH3/8wV133cWnn37K0qVLef/9\n96lXrx4tW7Zk+vTphISE0KVLlzzPlZqaSlhYWJ5t/IFAyQN8I5eWLVtuVtXb8m3oTl081nOmisBP\n9nZL4AN3+roYyy90WjliG4o1Oeabn9Fp+R6BkktR5HHs2DGNiopybK9Zs0bbtm2brU1ycrI2a9ZM\nVVXvvvtujYqK0qioKC1XrpxWqFBBJ06cmOc5zPfhe/hCLrip03K35D1DVU/ZVXtBqrpaRAp7wTGH\nTgs4LiJZOi13Cz5y6rTOq2qGXUgS7dRupVq3PRGRLJ3WoXzGfhyrEMNgCGhuuOEGatSoQXJyMnXr\n1mXlypU0aNCAEydOULlyZS5dusTIkSPp06cPAGvXrnX0HT58OGFhYfTt29db4RuKAe5OWikiEgas\nBT4RkRNYmqmC4C86LSsokcZAsKpudiM+g8GvSElJoXfv3mzfvh0RYdq0aYSEhNCkSRNUFRGhVq1a\nREZGMmHCBA4cOEBERARbtmxhw4YNTJpkjA8MRYu7k1YH4CxW5Vw3rNtrIwp4zlXAmyLytKpOgSt0\nWjOwFqa9F6uS0Hli2g88Zy9eWx0P6rSc6ArMKYLzGAxFTlZ5+8KFC7lw4QLp6el88803juMvvfQS\n5cqVo1+/fnTo0IH27duzfft2l2MNHz68iKI2FGfcXeU9TUSigNqqOkNEymA9e8oTEbkBmIB1m+88\nl32zXPlpjQX64gU/LRH5J/AUUEGdSudFZDzwHPCriHQFKqtq+cI4p8HgbXIrb89CVZk/fz6rVq3y\nUoQGw5W4awL5NFZ1XDhW1V11LEPEVnn0ybW0XVX3AJ1ztI8GjqtqNn9uVd0PxNjvPaLTEpFzWL5Z\ne3P06w/0t9v8HWiSW74Gg7+RW3l7lt5q7dq1VKlShdq1Lxs67Nu3jyZNmlC2bFlGjhzJPffc463w\nDcUUd28PPo91K249gKrutTVbeeFzFiS5Baqq6+zz5JVPV2BYPjkbnZYPEii5FGYe+xPacfHiRX78\n8UcmTpzI7bffTr9+/UhISHCIhnMullu1alUOHjxIxYoV2bx5Mw8//DA7duygbNmyhRKTweAO7k5a\n51X1QtYvdbuwIT+BVwzgqnjhESzRbmMgAtgoImvcjAMgFEhU1UEishgYibU2YgNgBpZ9CvY5mmDd\nlkwWkYmq6rJK0EmnFSIiSfZuZ51WFHAj1vM4V/0dOq1KlSox/wH/XxkgNTWV6QGQBwROLoWZR2Ji\nIr///jsRERGcPXuWxMREbrrpJmbPnk2rVq3IzMxk3rx5TJ48OVefpYoVKzJnzhzq1q17Vef2K++m\nPAiUPMC/cnF30vpORP4P65d6PNZznuUFPKfPlbar6u12u1RVjXXR5HFgoR2zq/4fAB8A1K1bV+Pi\n4txMxXdJTEwkEPKAwMnFE3mMHz+eqlWrUrduXRITE7nnnnuIi4vjq6++olGjRjz22GOOtidPniQ8\nPJwSJUrw66+/cvLkSR577DHHornezMMbBEoe4F+5uDtpDcay5dgGPAt8AUzNp49flbbnw+NYt0gN\nhoBi4sSJdOvWjQsXLlCrVi0++ugjAObOnZvt1iDAmjVrGDp0KMHBwZQoUYJJkyZd9YRlMFwr+a3y\nXhOsCUFVp6jqY6rayX6f3+1Bv7AgyQ8RqQtUwDKBNBgChpSUFEaOHElqaioZGRkMGjSIPn36EBsb\nS1JSEgkJCcTGWjceNmzYwBtvvEHJkiUBeO2113jwwQe9Gb6hmJLf1ccSoCmAiCxS1UfdHVhVVURc\nlba/CIThhdL23BCRMcBfgDIichiY6lSB2BWY68YkbTD4Fa40WvPmXa6JytJoAcTExLBp0yaCg4M5\nduwYjRs35sEHHyQ4+FpuYBgMV09+/+Kcb+XVKsD4Pm9BYpOBNYGmq2pk1k4R6YO1JmKmiNwNPKOq\nxgjS4PdcrUarTJkyjmPnzp0zrsQGr5Gfc7Hm8j5fnHRaiap6k6o2wCpPr3J1IRYJy3F9C3K2qjay\nizPGYK10bzD4Pc4arSZNmtC7d2/S0tIcx11ptNavX0/Dhg1p1KgRkyZNMldZBq+QpzWJiGQCaVhX\nSCFAetYhrAuiXAUafmpBkup8JZcj7q7AU6p6hT2rsSbxbQIll8LKo1H1crlakPTq1QuwqgqrV69O\n586dr+h/4MABEhISePvtt7NdnbmLL9hgFAaBkgf4Ri6Fak1SkBf+aUGS6mLf88AvWOXytfMbw1iT\n+B6Bkkth5pGXBUlGRoZWrlxZDx06lGv/uLg43bhxY4HObb4P38MXcsFNa5L8bg96AodOS1WPA1k6\nLXfJqdP6TlUz7PfRTu1WquofqnoOyNJpXTWq+p6q3gQMAl4tyBgGg6/hbEECOCxIAL799lvq1atH\nZKTj8S779u3j4kXL2OHAgQMkJycTHR1d5HEbDJ68KR1IOi2AucD71ziGweAzXI1G6/vvvychIYGS\nJUsSFBTEv/71LyIiIrwRtqGY48krLb/XaYlIbafNduRYUNdg8EdSUlLo1KkTjz/+OGlpaUyePJkl\nS5Ywa9Ys6taty8aNG/n111+z9WnRogUHDhzgiSee4Mcff+Thhx/2UvSG4o7HrrRUA0Kn1VdE7sMq\niT8NdPdkHAZDUeBKn7V69WqWLl3K1q1bKV26NCdOnMjWp3///rRpc0UNksFQ5Hi6ZtWvdVpYJe4x\nQCU79j/yGcdg8Gly02e9//77DB48mNKlreLaypUvmzgsWbKEWrVqOSxLDAZv4rFJKy8/LWCPp85b\nQJbjwk8LGAfMVMv48s/AKODJvAYy1iS+R6Dkcq157E9ol6uH1p49e1i7di1DhgzhuuuuY9y4cTRr\n1oy0tDRGjx7NihUrGDduXCFmYzAUjDx1Wtc0cADotERkB9BaVQ/bcf+hLrRpRqfl2wRKLteaR176\nrLVr19KkSRP+/ve/s3v3bkaMGMHs2bOZNGkS9erVo2XLlkyfPp2QkBC6dOlyTXn4giaoMAiUPMA3\ncjE6rULQaQGzgX72+0fsc1fMawyj0/I9AiWXwsgjN31W69ats41fq1YtPXHihN59990aFRWlUVFR\nWq5cOa1QoYJOnDjxmmIw34fv4Qu54KZOyxvrsPicn1YeDADeta/c1gBHsMrxDQa/xFmfVbduXYc+\n66abbmLVqlXExcWxZ88eLly4QEREBGvXrnX0HT58OGFhYfTt29eLGRiKO0anlQeqehTrCgsRCQMe\nzZoIDQZ/xZU+K2sJp5iYGEqVKsWMGTPMorgGn8STk9Yq4E0ReVpVp8AVOq0ZQDiWTmsg2Sem/cBz\nIhIEVMd7Oq0I4HdVvQS8AkzzRhwGQ2GQkpJC79692b59OyLCtGnT+Prrr4mJiaFSpUoAjBkzhrZt\n2wKwdetWnn32Wf73v/8RFBTExo0bue666/I6hcHgcYxOizx1WnHAKBFRrNuDxr3Y4Le40md9/fXX\n9O/fnwEDBmRre/HiRZ544gk+/vhjGjduzKlTpxwGkAaDNzE6LQuXOi1VXQgsFJFOwAJgErApn7EM\nBp8jP/+snHzzzTfccsstNG7cGICKFSsWRZgGQ74YnZZFbjotROR6rErI9e4MZHRavkeg5FLQPPLS\nZwG8++67zJw5k9tuu4233nqLChUqsGfPHkSE1q1bc/LkSR5//HFefvnlwk7JYLhqjE4rHz8tEZkA\nfItVSThAVa+40jI6Ld8mUHIpaB556bMefvhhypUr53jGderUKQYNGsS8efNYsmQJkyZNonTp0rz0\n0kv06tWLW2+99Zrz8AVNUGEQKHmAb+RidFqFo9NqAiyy3ydiTaTGT8vPCJRcriWPvPyzsti3b582\nbNhQVVXnzJmj3bt3dxwbMWKEjhkzpsDnd8Z8H76HL+SC8dO6Nj8tu3JxPPDS1fQzGHyR3Pyzjh07\n5mizePFiYmJiAGjdujVbt24lPT2dixcv8t133zn8tgwGb2J0WrlzPdZiuYm2XuUGYJmIPKQubhEa\nDL6OK33WCy+8QFJSEiJCdHQ0kydPBqBChQr84x//oFmzZogIbdu2pV27dl7OwGAwflq5Yl+lRahq\ntKpGA+sAM2EZfJ4sv6x69epRv359fvjhB1577TWeeuopLl68yA033MC//vUvKlSoQKdOnRARgoKC\nOHr0KL/88otjnCeeeIIdO3awfft2xowZ48WMDIbLeGzSsq+GngFeE5EMETkHfA78B2vJpi1YE9vL\nqvpfIBK42e7urNMahwd1WiJSRkR+EZEMIFRE/iciw+1jfURkm4gkYT3futFTcRgMhUWWHmv37t1s\n2bKF+vXrM3DgQLZu3UpSUhLt27dnxIgRALRq1YotW7aQlJTEtGnT6N27t5ejNxjyxtMl75OAN1V1\nkr0vFrheXeu0DgM/g1d0Wr1VdbWIlAJWcrm8fbZT7A8Bz2HptQwGn8QdPVZaWppjiSbnijHn/QaD\nr+LJZ1otsZ4/TcraoapJYjGWHCXvzh09VfLuKkhVTQdW2+8viMiPWFd9qOr/nJqG2vHmidFp+R6B\nkkt+eeSlxwoNDWXIkCHMnDmTcuXKsXr1ake/xYsX88orr3DixAk+/9z/PydDYONJndYLwI2q2j/H\n/keBPsADQASwEbgdSz/ljk5Lgbaq+qWILMaaTNoBDbCEzLF2/6FYt/TOA8lYVYsLyVunVR7rVuR9\nqvqrve954B9AKeDPqupKgOzQaVWqVOnW+fPnF/BT8x18QbdRWARKLu7kkZseq1evXo42n3zyCRcu\nXKBnz57Z+m7ZsoWZM2fy1ltveST+LIrT9+Ev+EIuvqzTGg/0ctr+GOuqKBr3dFrnuTzZjgCG2O+D\ngBSn/lOc+n8J3J1PvMF2uxdzOf4XrEnR6LT8jEDJxZ083NFj7d+/36HHykl0dLSePHnyWsLMl+L0\nffgLvpALPqDT2gG4ks97rOSd7Lc7r7bk/QNgr6pOyOX4XODhfMYwGLxKbnqsvXsv3yBYtmwZ9erV\nA+Dnn3/O+qOMH3/8kQsXLph1Bg0+jbEmseIaibV6Ru8c+2vr5duB7XCxNqHB4G1yWo4MHjyYtm3b\ncuTIEc6fP8/KlSsZPHgwycnJBAUFUaFCBVJTU2nYsCGnTp0iPDycUqVKERISwrx580wxhsGnKfbW\nJCISCQwBdgM/2v9h31XVqUBfEbkPaxX400B3T8VhMBQUV5Yjf/rTnwgKCuLZZ5+lbNmyLFq0CLAs\nR5o2bZrNcqR8+fKUKFHCy1kYDO5R7K1JVPWwiLwJPIW1PqHz08gDdsxBdh6puY1jMHiD3Ercy5cv\n77K9sRwx+Dsee6blZE2SqKo3qWoDrPL0Kp465zWwHNe3IH/CqmK8Bavy0CwLYPApnEvcmzRpQu/e\nvUlLS8u1vbPlSNOmTc1KFwa/o1jptPKwJllnnyfbAVVd7bS5DnjCVaI5rEmY+MlSNz4e36ZKCAGR\nBwROLjnzyLIc2bx5Mz169KBHjx5MnDiRv/3tb44S95SUFDZv3kxqqnWTIDk5mW+//Tab5UiJEiUK\nxXLEXVJTU0lMTCyy8zb+rJgAABpzSURBVHmKQMkD/CwXd0oMC/IiAKxJchx7F3g1vzFMybvvESi5\nuMojvxL3Fi1a6MaNGx3bnrQccZdA/j78FV/IBR8oec8Nv7AmcUZEngBuA8YWdAyDwRPkVuKeG8Zy\nxODvGGuSfLCrB4cALVT1fH7tDYaixpXlyOLFi/n73//OyZMnadeuHbGxsXz99dfGcsTg9xhrkjwQ\nkSbAZCxLkhPeiMFgcCY6OppGjRoRGxvLbbdZK96ICCVLlkRVyczMpESJEnTs2JF9+/bx+OOPU7ly\nZQ4ePMioUaMAYzli8G88bU3SEYi3rT92AMOB2bi2JnGmyKxJAERkjIgcBsqIyOEsaxKs24FhwAIR\nSRKRZZ6Mw2Bwh9WrV5OUlMSmTZa1W+/evUlISGDbtm107NiRsWOtu9gLFizg/PnzbNu2jc2bNzN5\n8mT279/vxcgNhmun2Ou0RKQM0BBLg7ULWO407iyyP8cyk5bB50hOTubee+8FID4+ntatW/PGG28g\nIqSlpXHx4kXOnj1LqVKlKFu2rJejNRiuDU/7aS3GWmT2cXtfLFbF4B5PnbeAjFMnPy0RaaOqX9rH\n5qldeu8OxprE9wiEXPYnWM+dRIT7778fEeHZZ5/lmWeeISYmhmXLltGhQwcWLFjAoUOHAOjUqRNL\nly6latWqpKenM378eMLDw72ZhsFwzRidlqXTcumn5S45dVpDG128mu4+SZUQ65d9IBAIuSQmJpKa\nmsrYsWOJiIjg9OnTDBgwgLNnz9KnTx9GjhzJwIEDueuuuwgKCiIxMZFt27bx22+/MWfOHM6cOUO/\nfv0ICwujWrVqXs3FrzRBeRAoeYCf5eJOXXxBXvinTqu83a+W0zjHsJ7BLXRnDKPT8j0CJZeceQwb\nNkzHjh2bbV9ycrI2a9ZMVVWfe+45nTlzpuNYz549dd68eR6PMz8C9fvwZ3whF4xO6+p0Wna5/Bzg\nHbUNILGWd4pWaxmnb4EZVxGnwVConD17ljNnzgCQlpbGN998Q0xMDCdOWIWtly5dYuTIkfTp0weA\nmjVrsmrVKlSVtLQ01q1b57AkMRj8FeOndZkr/LRU9ZRe1mZNwXU+BkORcPr0ae6++24aN27Mn/70\nJ9q1a8cDDzzAnDlzqFOnDvXq1aNatWoOR+Lnn3+e1NRUYmJiaNasGT179uSWW27xchYGw7Vh/LTI\n00+rqqoeszcfwqouNBg8TnR0NNdffz0lSpQgODiYTZs2MWXKFMf6mGlpaSxYsIAhQ4bQr18/+vXr\nx8GDB2nQoAEREREMGDCAsLAwFixY4OVMDIbCxfhp5e2n9YKIPIR15fc71jMug6FIWL16NREREY7t\nYcOGERcXB8BLL71EuXLlsrXv378/bdq0KcoQDYYip9jrtDRvP60vgdbALcDjqro7t3EMhqJCVZk/\nfz6rVq1y7FuyZAm1atUiNDTUi5EZDJ7H6LQslmOt4r43x/6DWFdXA9wdyOi0fA9/yiUvPVYWa9eu\npUqVKtSuXRuwbhWOHj2aFStWMG7cOK/EbTAUFUanlbef1n57/yXywOi0fBt/yiVLK+NKj3XTTTeR\nmJjI+PHj+f/tnXt4VdWVwH+LpCoIBhBFCRpARIooUNFCfQWVFJ0O1PFRFB3RYrV+yKhVC83IoFON\niHV4hBo71FeVR6VWQaf4gAQtUhBtggRFUVOJaEAoTwUDrPlj73s5CTchQG7uOcn6fd/57j777HPu\nWnff3J2993qcddZZ8baPPvooOTk5LFu2jLKyMpo3bx5qn5tI+QTVQmPRAyKmS13s4g/mIJp+Wgnz\naeGWGi+vi97mpxU+oq5LzB+rsLBQKysr9dhjj9U1a9bEr59zzjmalZWlWVlZmpGRoW3atNEpU6ak\nUOLaiXp/xGgseqiGQxfq6KeV7D2tRMT9tIAKEYn5aS2v4/3V/bR2qmqliCT00wIQkZif1pp6kN8w\nksr27dvZs2cPrVq1ivtjjR07FoDXX3+d7t2707Hj3qAtb775Zrw8btw4WrZsyciRdY48ZhiRwvy0\nDCNkVFRUJPTHApg5cyZXXXVViiU0jNRh+bQMI4Ukyo/VpUsXSkpKuPbaa1m5ciU33XQT4Aas4uJi\nCgoK6NmzJ2lpaWzcuLHK88aNG8edd9bZbsgwIkey/bR+BswVkd/iZjtbcKbsify0zgG6+tsb0k+r\nhX+fE4F0EdkCPKKq40TkB7jwTUcAw0TkAVXtlixZjKZJdX8sgDVr1vDaa69x4oknxuuGDh1KQYGz\na5o7d65FbTeaJMk2eS8AHlBvQehN3ltpYj+tcmA1NKyflmeEBlKTAEt8fW/gaVW9WUSG4pJaGkbS\nuf3223nooYcYMmRIwuszZsywZUKjSdKkTN4TCamqXwM1pSYZgsu2DC7Ke76ISGBPbR/MTyt8hFGX\n2vyx5syZQ2ZmJr169Up479dff828efPIz89vSJENIxQkc9DqCbyToP7fcDOYXkA74G0ReeMAnnsk\nUKSqvxSRPwO/BgYCPXBR2GPZhXsDfXAGGatEZApu4Enkp/UegIi0Bv4VmOSvZeItDlV1l4hsBo4G\nvgo+wPy0wk0YdanNH6ugoIAJEyZQVFTEjh07WLRoERkZGXFfmgULFtC9e3eWL6+rwW24iJRPUC00\nFj0gWro0KZN3Vf1+TQ+tITVJIkvHfWZZqvo7XJR4TjnlFL11WOIlnShRVFTElT7OXdSJii4lJSVs\n2bKFDRs2xE3Wv/rqK2699VaWLl3KBx98QHZ2NpMmTWLkyJHxOIRRo6ioKLKyB2ksekC0dDGT973s\nk5oEt892AsQHtQxc4FzDOGS2b9++T36sM888k3Xr1lFWVkZZWRkdO3bk3Xff5bjjjgNg8+bNLFy4\nsMa9LsNo7JjJO1VSk9xW7dIc4DpfvhxYUNt+lmHUhZiZe9++fWnfvj29evWiU6dOfPLJJ4wePZqc\nnBzWrl0LuMSOw4YNo1evXgwfPpw77riDnJwcC4xrNFksNUntqUl+D/xBRFbjZlhDkyWH0bSobua+\nZcsWjjrqKAAmT57MfffdR0FBATfffDObN29m/PjxvPDCC9xwww18+eWXqRLbMFJOUve0VHUtcGX1\nehH5DW7Z7Uzgv0TkOpzlX09/X9zk3Q9mL6lqkb9W76lJqHnJcghwCm6psSiw12UY9UpswAK3VBgL\n3iwibN26FVXlm2++oW3btqSnW3AXo+nS4N/+qKQsEZGjgQnAGaq6XkSeEpELVXV+qmUzok1NaUdy\nc3N5+umnycjIoLCwEICRI0cyePBgOnTowKZNm5g9ezbNmiVzVd8wwk0q/mVLpf/WxziT9WY40/dN\nwEUxk/dqdAE+VNX1/vx1XIT6Wgct89MKH2HRJeabtWjRIjp06MC6desYOHAg3bt357zzzuP+++/n\n/vvvJy8vj/z8fO69915eeeUVevfuzYIFC5g+fTojR47k3HPPrTIzM4ymRCoGrVT6b/03MJaA/xZu\n4ErEaqC7X54sB34MHJaoYdBP65hjjuGPg6K/Sb5t2zaebAR6QHh0CfrBfPihW1To06cPM2bMYM+e\nvSnbOnfuzJgxYxgwYAAPP/wwV199NQsXLiQjI4M2bdrw7LPP8t3vfrehxa83ouQTVBuNRQ+Ili5h\nWhwPVcoSVf2niPwcmIUzrX8LN/vah+p+WlHxd6iNKPlt7I8w6VI97civfvUrxo4dS2ZmZjwT8ZQp\nUzjjjDPIzs6mT58+bNy4kezsbJ5//nkqKiq44oor9olVGCXC1B+HQmPRA6KlSyoGrVKc+Xh1kua/\n5X2sYtTZf0tV5wJzIT6b2l0HGQ2jRioqKrj0UhfCcteuXVx99dUMGjSIyy67jFWrVtGsWTOysrLi\ngXHvuecehg8fzmmnnca2bdsYP358pAcswzhUUjFoLQAeEJEbVfV/YR//raeAtjj/rbuoOjCVAbeI\nSDNciKVk+28dq6rrRKQNcAsJLCENY3906tSJVq1akZaWRnp6OiUlJdx1113MnTuXWbNmsWzZMp54\n4glat24NQF5eHueffz5paWlMnjyZV199FYjWf8OGkSwa3AzJz4YuBQaKyMciUooLSjsdtxRYghvY\n7lbV6g4pQf+th0mi/5Znkl9CXAQ8qKqhsW40okVhYSHFxcUsW7YMgIEDB7JixQqWL19Ot27dyMvL\nA2DlypXMnDmT0tJS5s2bxy233MLu3TbBN4wYSZ1pichxwETc3tROvHOx//G/slrbTsDFMV+tGKpa\nhjPeSErKEhFpISIv46LB7wbmqupo3+RtnGHILuBGEVmsqv+om/aGUTM5OTnxcr9+/Zg9ezYAL774\nIkOHDuXwww+nc+fOdO3alaVLl9K/f/9UiWoYoSLZ+bRC74/leTiYT0tELlbVvwB/x5nYf+2NMh4C\nflLbg8zkPXykSpfa0o8Eefzxx/nJT9zX6vPPP6dfv37xax07duTzzz9vOKENI+RYPi03ID0oIrGU\nJZnAYyLyL6paGGj6N+CaRM+w1CThJlW61JZ+JJYr65lnnmHTpk1kZmZSVFREeXk577//fvzeL774\ngtLSUtq1axcps+TaMD3CR5R0aVL5tFR1H9N2gFjKEp9P612cw3H1kE0/Bf5Sw/2WmiTEhEmXkpIS\nKisryc7O5qmnnqK0tJT58+fTokULABYvXgwQN7jIy8sjJyeH/v37NxpDDNMjfERJl1TEg4n7Y6lq\nBRDzx6or1f2xFqpqpS93CrSbr6qbVXUHEPPHqpEa8mnFrl0D9MWFdTKMOpMo/UjPnj2ZN28e48eP\nZ86cOfEBC2Dw4MHMnDmTnTt38umnn/LRRx9x1llJNZI1jEiRzJlWZPyxPInyaSEiF+GiwJ+vqjsT\n3mkYNVCTX1bXrl3ZuXMnAwcOBJwxRkFBAaeeeipXXnklPXr0ID09nalTp5KWlpZKFQwjVCRz0IqS\nP1Ysn9aIavV9gMeAQaq6LpkyGKlh9+7d9O3bl8zMTF566SXy8/OZOHEiH3/8MevXr4878m7evJlr\nrrmGzz77jF27dnHnnXdy/fXX7/f5Xbp0oaSkZJ/61atX13hPbm4uubm5B6+UYTRikrY8GBV/rEA+\nrR64fFrFIhIbvCbgcn895+vn1PQcI5pMmjSpShy/s88+m9dff52srKqryVOnTqVHjx6UlJRQVFTE\nL37xC7799tuGFtcwmjzJjoixxx/glgUFEFW9Cze7Skgy/LFqkXEj8H84K8Pv4Py0pvn7LhKRK3GD\nrQLbanmOETHKy8t5+eWXyc3N5ZFHHgFcANtEBPNabdu2zfJaGUaKMD8tR0I/LRE5GRgDnO0D6B67\nvweZn1b4qK5LzH/qtttu46GHHoobStRGMK/V1q1bmTVrluW1MowU0KT8tERkCS6PVpBrY/5Yqvqt\niLwLdPTXbgSmquo//fWE+1rmpxVuqutSVFTE4sWLqaysZOvWrRQXF7Nhw4Yqfio7duxg0aJFZGRk\nALBw4ULatWvH9OnTWbt2LSNGjGDatGkceWTDpTyJki9NbZge4SNSuqhqUg5gFPA/CeovA14D0nCz\nrs+A43Hm6it8m+FAfuCel4BsX1ZcuCdwM7lXcct6vYDiwP2f4IwrjgD+AZxQB5lb+/u6+PMXcAPi\nIpxz8aD9PaNbt27aGCgsLEy1CPVGIl1Gjx6tmZmZmpWVpe3bt9fmzZvrsGHD4tezsrJ0/fr18fNL\nLrlE33jjjfj5gAEDdMmSJUmVuzqNpU9Mj/ARBl2AZVqHscX8tDw1+GmlAycD2cBVwDTvgGxEnLy8\nPMrLyykrK2PmzJlccMEFPPPMMzW2P/HEE5k/3yWtrqioYNWqVXTpkjC9mmEYSSSZg1YpcEaC+qT5\naVF1ubM+/LTKgRdVtVJVP8VlOj65DvIbEWXy5Ml07NiR8vJyTj/9dEaMcIak99xzD2+99RannXYa\nF154oeW1MowUYX5a1OynhVsevAp4UkTaAd1wy4dGIyI7OzsewmbUqFGMGjVqnzYdOnSI57UyDCN1\nJG3QUlUVkUuBiSIyGtiBT02C830qwe1P3a2qX/rUJDGCfloraBg/rQ9wflrg9tOmAa8AOT6n1m7g\nLlXdkCxZDMMwjNpJqqOJqq4lcbbfffy0NMl5s2qRsZwaliy9HHf4wzAMw0gx5mhiGIZhRAbZa9PQ\n+KnFT+u9enyPrTiDjajTDvgq1ULUE41FF9MjXDQWPSAcumSp6jH7a9SkBq2GQESWqWrfVMtxqDQW\nPaDx6GJ6hIvGogdESxdbHjQMwzAigw1ahmEYRmSwQav++V2qBagnGose0Hh0MT3CRWPRAyKki+1p\nGYZhGJHBZlqGYRhGZLBByzAMw4gMNmjVIyIySERWichqH7oqtIjICSJSKCLvi0ipiPyHr28rIq+J\nyEf+tY2vFxGZ7HVbLiLfS60GVRGRNBH5u8+9hoh0FpElXo9ZPsEnInK4P1/tr3dKpdxBRKS1iMwW\nkQ98v/SPYn+IyO3+O7VCRGaIyBFR6Q8ReVxE1onIikDdAfeBiFzn238kIteFRI8J/ru1XET+HMxY\nISJjvB6rROSHgfrw/abVJX+JHXXKH5YGfAx0AQ7DxVbskWq5apH3eOB7vtwKl026By5/2GhfPxoY\n78uXAH/BhbzqByxJtQ7V9LkDmA685M//CAz15QLg5758C1Dgy0OBWamWPaDDU8AIXz4Ml98tUv2B\nC3D9KdA80A/Do9IfuADe38Pn9vN1B9QHuEDgn/jXNr7cJgR65ADpvjw+oEcP/3t1ONDZ/46lhfU3\nLeVf8sZyAP2BVwLnY4AxqZbrAOR/ERiIi+ZxvK87Hljly48BVwXax9ul+sBlmp4PXIBLGCo47/7Y\nH2i8b3BBkPv7crpvJyHQ4Sj/Yy/V6iPVH37QWuN/sNN9f/wwSv1BICHtwfQBLjPEY4H6Ku1SpUe1\na5cCz/pyld+qWJ+E9TfNlgfrj9gfa4xyXxd6/JJMH2AJ0F5VvwDwr8f6ZmHWbyJwNy6/GsDRwCZV\n3eXPg7LG9fDXN/v2qaYLsB54wi9zThORI4lYf6jq58DDuIzkX+A+33eIXn8EOdA+CGXfVOMG3CwR\nIqaHDVr1R6JI8aH3JxCRlsCfgNtUdUttTRPUpVw/EfkRsE5V3wlWJ2iqdbiWStJxyzmPqmofYDtu\nKaomQqmH3+8Zgltm6gAcCVycoGnY+6Mu1CR7qHUSkVxcot1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a978df6ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 2 log loss 0.26009641063456906 in 2179\n",
      "Fold :  3\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.531651\tvalid_1's multi_logloss: 0.541929\n",
      "[200]\ttraining's multi_logloss: 0.363791\tvalid_1's multi_logloss: 0.377743\n",
      "[300]\ttraining's multi_logloss: 0.303882\tvalid_1's multi_logloss: 0.319165\n",
      "[400]\ttraining's multi_logloss: 0.279958\tvalid_1's multi_logloss: 0.295547\n",
      "[500]\ttraining's multi_logloss: 0.269476\tvalid_1's multi_logloss: 0.285049\n",
      "[600]\ttraining's multi_logloss: 0.264436\tvalid_1's multi_logloss: 0.27997\n",
      "[700]\ttraining's multi_logloss: 0.261772\tvalid_1's multi_logloss: 0.277321\n",
      "[800]\ttraining's multi_logloss: 0.26023\tvalid_1's multi_logloss: 0.275868\n",
      "[900]\ttraining's multi_logloss: 0.259257\tvalid_1's multi_logloss: 0.275027\n",
      "[1000]\ttraining's multi_logloss: 0.258619\tvalid_1's multi_logloss: 0.274555\n",
      "[1100]\ttraining's multi_logloss: 0.258174\tvalid_1's multi_logloss: 0.274267\n",
      "[1200]\ttraining's multi_logloss: 0.257845\tvalid_1's multi_logloss: 0.274098\n",
      "[1300]\ttraining's multi_logloss: 0.257592\tvalid_1's multi_logloss: 0.273997\n",
      "[1400]\ttraining's multi_logloss: 0.25738\tvalid_1's multi_logloss: 0.273928\n",
      "[1500]\ttraining's multi_logloss: 0.25719\tvalid_1's multi_logloss: 0.2739\n",
      "[1600]\ttraining's multi_logloss: 0.257012\tvalid_1's multi_logloss: 0.273889\n",
      "[1700]\ttraining's multi_logloss: 0.256847\tvalid_1's multi_logloss: 0.273886\n",
      "[1800]\ttraining's multi_logloss: 0.256681\tvalid_1's multi_logloss: 0.273882\n",
      "[1900]\ttraining's multi_logloss: 0.256514\tvalid_1's multi_logloss: 0.273897\n",
      "[2000]\ttraining's multi_logloss: 0.256361\tvalid_1's multi_logloss: 0.273902\n",
      "[2100]\ttraining's multi_logloss: 0.256198\tvalid_1's multi_logloss: 0.273906\n",
      "[2200]\ttraining's multi_logloss: 0.256047\tvalid_1's multi_logloss: 0.273908\n",
      "[2300]\ttraining's multi_logloss: 0.255895\tvalid_1's multi_logloss: 0.273927\n",
      "[2400]\ttraining's multi_logloss: 0.25575\tvalid_1's multi_logloss: 0.273927\n",
      "[2500]\ttraining's multi_logloss: 0.255616\tvalid_1's multi_logloss: 0.273932\n",
      "[2600]\ttraining's multi_logloss: 0.255479\tvalid_1's multi_logloss: 0.273948\n",
      "[2700]\ttraining's multi_logloss: 0.255341\tvalid_1's multi_logloss: 0.273948\n",
      "[2800]\ttraining's multi_logloss: 0.255203\tvalid_1's multi_logloss: 0.273964\n",
      "Early stopping, best iteration is:\n",
      "[1825]\ttraining's multi_logloss: 0.256636\tvalid_1's multi_logloss: 0.273877\n"
     ]
    },
    {
     "data": {
      "image/png": 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cw8KFC1myZAlpaWksXryYjz76yNE+M+W9c+fOtGnTxouWG/KLR3fEUNVfyZba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\nHAWKgWJVfcsvv4QLYslw/QEuB3aqaom6iuZLgJ+SXPcA1P1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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a97d5c6898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 3 log loss 0.2738766988415174 in 1825\n",
      "Fold :  4\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.533864\tvalid_1's multi_logloss: 0.524894\n",
      "[200]\ttraining's multi_logloss: 0.366582\tvalid_1's multi_logloss: 0.354437\n",
      "[300]\ttraining's multi_logloss: 0.306791\tvalid_1's multi_logloss: 0.293454\n",
      "[400]\ttraining's multi_logloss: 0.282878\tvalid_1's multi_logloss: 0.269189\n",
      "[500]\ttraining's multi_logloss: 0.272373\tvalid_1's multi_logloss: 0.258771\n",
      "[600]\ttraining's multi_logloss: 0.26731\tvalid_1's multi_logloss: 0.253796\n",
      "[700]\ttraining's multi_logloss: 0.264623\tvalid_1's multi_logloss: 0.251249\n",
      "[800]\ttraining's multi_logloss: 0.263068\tvalid_1's multi_logloss: 0.249831\n",
      "[900]\ttraining's multi_logloss: 0.262096\tvalid_1's multi_logloss: 0.249038\n",
      "[1000]\ttraining's multi_logloss: 0.26145\tvalid_1's multi_logloss: 0.248594\n",
      "[1100]\ttraining's multi_logloss: 0.260996\tvalid_1's multi_logloss: 0.248331\n",
      "[1200]\ttraining's multi_logloss: 0.260661\tvalid_1's multi_logloss: 0.248178\n",
      "[1300]\ttraining's multi_logloss: 0.260401\tvalid_1's multi_logloss: 0.248112\n",
      "[1400]\ttraining's multi_logloss: 0.260187\tvalid_1's multi_logloss: 0.248066\n",
      "[1500]\ttraining's multi_logloss: 0.259992\tvalid_1's multi_logloss: 0.248048\n",
      "[1600]\ttraining's multi_logloss: 0.259813\tvalid_1's multi_logloss: 0.248045\n",
      "[1700]\ttraining's multi_logloss: 0.259635\tvalid_1's multi_logloss: 0.248044\n",
      "[1800]\ttraining's multi_logloss: 0.259467\tvalid_1's multi_logloss: 0.248051\n",
      "[1900]\ttraining's multi_logloss: 0.259303\tvalid_1's multi_logloss: 0.248063\n",
      "[2000]\ttraining's multi_logloss: 0.259142\tvalid_1's multi_logloss: 0.248086\n",
      "[2100]\ttraining's multi_logloss: 0.25898\tvalid_1's multi_logloss: 0.248114\n",
      "[2200]\ttraining's multi_logloss: 0.258827\tvalid_1's multi_logloss: 0.248134\n",
      "[2300]\ttraining's multi_logloss: 0.258678\tvalid_1's multi_logloss: 0.248147\n",
      "[2400]\ttraining's multi_logloss: 0.258532\tvalid_1's multi_logloss: 0.248176\n",
      "[2500]\ttraining's multi_logloss: 0.258392\tvalid_1's multi_logloss: 0.248195\n",
      "[2600]\ttraining's multi_logloss: 0.258255\tvalid_1's multi_logloss: 0.248196\n",
      "Early stopping, best iteration is:\n",
      "[1679]\ttraining's multi_logloss: 0.25967\tvalid_1's multi_logloss: 0.248038\n"
     ]
    },
    {
     "data": {
      "image/png": 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SJLlQEkpPlCTMH4GEyx95lRtZsmQJbdu25eGHH+aHH37g+eefZ+rUqbz99ts0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\naYlIGvAzf3ioTmsD0a2n1dCfpyNQV0R+BB5Sl04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JvpRSQ2AHCXyNHCsWtKqO9sCXIdtf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\nYRiGETf8P2j3XhxAv/cIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a97c09e470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 4 log loss 0.24803842380021507 in 1679\n",
      "Fold :  5\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.531912\tvalid_1's multi_logloss: 0.539614\n",
      "[200]\ttraining's multi_logloss: 0.364194\tvalid_1's multi_logloss: 0.373995\n",
      "[300]\ttraining's multi_logloss: 0.304347\tvalid_1's multi_logloss: 0.314496\n",
      "[400]\ttraining's multi_logloss: 0.280445\tvalid_1's multi_logloss: 0.290781\n",
      "[500]\ttraining's multi_logloss: 0.269958\tvalid_1's multi_logloss: 0.280429\n",
      "[600]\ttraining's multi_logloss: 0.264894\tvalid_1's multi_logloss: 0.275468\n",
      "[700]\ttraining's multi_logloss: 0.26222\tvalid_1's multi_logloss: 0.272902\n",
      "[800]\ttraining's multi_logloss: 0.260664\tvalid_1's multi_logloss: 0.271485\n",
      "[900]\ttraining's multi_logloss: 0.259685\tvalid_1's multi_logloss: 0.270685\n",
      "[1000]\ttraining's multi_logloss: 0.259046\tvalid_1's multi_logloss: 0.270217\n",
      "[1100]\ttraining's multi_logloss: 0.258604\tvalid_1's multi_logloss: 0.26993\n",
      "[1200]\ttraining's multi_logloss: 0.258283\tvalid_1's multi_logloss: 0.269771\n",
      "[1300]\ttraining's multi_logloss: 0.258027\tvalid_1's multi_logloss: 0.26968\n",
      "[1400]\ttraining's multi_logloss: 0.25782\tvalid_1's multi_logloss: 0.269635\n",
      "[1500]\ttraining's multi_logloss: 0.257625\tvalid_1's multi_logloss: 0.269604\n",
      "[1600]\ttraining's multi_logloss: 0.257453\tvalid_1's multi_logloss: 0.26959\n",
      "[1700]\ttraining's multi_logloss: 0.257282\tvalid_1's multi_logloss: 0.269593\n",
      "[1800]\ttraining's multi_logloss: 0.25712\tvalid_1's multi_logloss: 0.269603\n",
      "[1900]\ttraining's multi_logloss: 0.256965\tvalid_1's multi_logloss: 0.269607\n",
      "[2000]\ttraining's multi_logloss: 0.256815\tvalid_1's multi_logloss: 0.269637\n",
      "[2100]\ttraining's multi_logloss: 0.256662\tvalid_1's multi_logloss: 0.269651\n",
      "[2200]\ttraining's multi_logloss: 0.256517\tvalid_1's multi_logloss: 0.269669\n",
      "[2300]\ttraining's multi_logloss: 0.25637\tvalid_1's multi_logloss: 0.269672\n",
      "[2400]\ttraining's multi_logloss: 0.256223\tvalid_1's multi_logloss: 0.269682\n",
      "[2500]\ttraining's multi_logloss: 0.256083\tvalid_1's multi_logloss: 0.2697\n",
      "[2600]\ttraining's multi_logloss: 0.255948\tvalid_1's multi_logloss: 0.269716\n",
      "[2700]\ttraining's multi_logloss: 0.255813\tvalid_1's multi_logloss: 0.26972\n",
      "Early stopping, best iteration is:\n",
      "[1717]\ttraining's multi_logloss: 0.257255\tvalid_1's multi_logloss: 0.269588\n"
     ]
    },
    {
     "data": {
      "image/png": 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69fJff/nll/uP27dvT1ZWVonaa5QsJf7OK6BUSqqqNlDVpri09sSStiUMXsBl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\nFOl2t2P/M4Aifz+sAXrF2r0ALMKpsZcCL+Kkl7r9vQC8gnv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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a96f87ddd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 5 log loss 0.2695882132699292 in 1717\n",
      "Fold :  6\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.530226\tvalid_1's multi_logloss: 0.550842\n",
      "[200]\ttraining's multi_logloss: 0.362098\tvalid_1's multi_logloss: 0.390075\n",
      "[300]\ttraining's multi_logloss: 0.302173\tvalid_1's multi_logloss: 0.333357\n",
      "[400]\ttraining's multi_logloss: 0.278272\tvalid_1's multi_logloss: 0.310659\n",
      "[500]\ttraining's multi_logloss: 0.267807\tvalid_1's multi_logloss: 0.300548\n",
      "[600]\ttraining's multi_logloss: 0.262775\tvalid_1's multi_logloss: 0.295699\n",
      "[700]\ttraining's multi_logloss: 0.26013\tvalid_1's multi_logloss: 0.293153\n",
      "[800]\ttraining's multi_logloss: 0.258601\tvalid_1's multi_logloss: 0.29169\n",
      "[900]\ttraining's multi_logloss: 0.257644\tvalid_1's multi_logloss: 0.29082\n",
      "[1000]\ttraining's multi_logloss: 0.25702\tvalid_1's multi_logloss: 0.29033\n",
      "[1100]\ttraining's multi_logloss: 0.256595\tvalid_1's multi_logloss: 0.290062\n",
      "[1200]\ttraining's multi_logloss: 0.256283\tvalid_1's multi_logloss: 0.289902\n",
      "[1300]\ttraining's multi_logloss: 0.256039\tvalid_1's multi_logloss: 0.289806\n",
      "[1400]\ttraining's multi_logloss: 0.255834\tvalid_1's multi_logloss: 0.289762\n",
      "[1500]\ttraining's multi_logloss: 0.255662\tvalid_1's multi_logloss: 0.289737\n",
      "[1600]\ttraining's multi_logloss: 0.255494\tvalid_1's multi_logloss: 0.28973\n",
      "[1700]\ttraining's multi_logloss: 0.255342\tvalid_1's multi_logloss: 0.289731\n",
      "[1800]\ttraining's multi_logloss: 0.255183\tvalid_1's multi_logloss: 0.289742\n",
      "[1900]\ttraining's multi_logloss: 0.255038\tvalid_1's multi_logloss: 0.289744\n",
      "[2000]\ttraining's multi_logloss: 0.254895\tvalid_1's multi_logloss: 0.289755\n",
      "[2100]\ttraining's multi_logloss: 0.254746\tvalid_1's multi_logloss: 0.289764\n",
      "[2200]\ttraining's multi_logloss: 0.254604\tvalid_1's multi_logloss: 0.289742\n",
      "[2300]\ttraining's multi_logloss: 0.25447\tvalid_1's multi_logloss: 0.289778\n",
      "[2400]\ttraining's multi_logloss: 0.254331\tvalid_1's multi_logloss: 0.289784\n",
      "[2500]\ttraining's multi_logloss: 0.254196\tvalid_1's multi_logloss: 0.289776\n",
      "Early stopping, best iteration is:\n",
      "[1567]\ttraining's multi_logloss: 0.255549\tvalid_1's multi_logloss: 0.289718\n"
     ]
    },
    {
     "data": {
      "image/png": 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Bkqiq56lqS4wN3Ys/+XGq2gLjKvyTiPRw9v+EMW+8FylhFovl1PC3rbdv356B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\nkY1WYNcQaOl/iCUsmmJoHAYsDVoXrtHagnPTSPf31Q/DcF/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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a97d3ce470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 6 log loss 0.2897184396785745 in 1567\n",
      "Fold :  7\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.532558\tvalid_1's multi_logloss: 0.534694\n",
      "[200]\ttraining's multi_logloss: 0.364969\tvalid_1's multi_logloss: 0.36733\n",
      "[300]\ttraining's multi_logloss: 0.305166\tvalid_1's multi_logloss: 0.307291\n",
      "[400]\ttraining's multi_logloss: 0.281278\tvalid_1's multi_logloss: 0.283316\n",
      "[500]\ttraining's multi_logloss: 0.270792\tvalid_1's multi_logloss: 0.272764\n",
      "[600]\ttraining's multi_logloss: 0.26574\tvalid_1's multi_logloss: 0.26774\n",
      "[700]\ttraining's multi_logloss: 0.263057\tvalid_1's multi_logloss: 0.265187\n",
      "[800]\ttraining's multi_logloss: 0.261496\tvalid_1's multi_logloss: 0.263822\n",
      "[900]\ttraining's multi_logloss: 0.260529\tvalid_1's multi_logloss: 0.263065\n",
      "[1000]\ttraining's multi_logloss: 0.259898\tvalid_1's multi_logloss: 0.262651\n",
      "[1100]\ttraining's multi_logloss: 0.25946\tvalid_1's multi_logloss: 0.262396\n",
      "[1200]\ttraining's multi_logloss: 0.259142\tvalid_1's multi_logloss: 0.262262\n",
      "[1300]\ttraining's multi_logloss: 0.25889\tvalid_1's multi_logloss: 0.262181\n",
      "[1400]\ttraining's multi_logloss: 0.258681\tvalid_1's multi_logloss: 0.262151\n",
      "[1500]\ttraining's multi_logloss: 0.258486\tvalid_1's multi_logloss: 0.262129\n",
      "[1600]\ttraining's multi_logloss: 0.258311\tvalid_1's multi_logloss: 0.262104\n",
      "[1700]\ttraining's multi_logloss: 0.258147\tvalid_1's multi_logloss: 0.262096\n",
      "[1800]\ttraining's multi_logloss: 0.257983\tvalid_1's multi_logloss: 0.262095\n",
      "[1900]\ttraining's multi_logloss: 0.257818\tvalid_1's multi_logloss: 0.26209\n",
      "[2000]\ttraining's multi_logloss: 0.257666\tvalid_1's multi_logloss: 0.262102\n",
      "[2100]\ttraining's multi_logloss: 0.257515\tvalid_1's multi_logloss: 0.262107\n",
      "[2200]\ttraining's multi_logloss: 0.257371\tvalid_1's multi_logloss: 0.262114\n",
      "[2300]\ttraining's multi_logloss: 0.257223\tvalid_1's multi_logloss: 0.262109\n",
      "[2400]\ttraining's multi_logloss: 0.257082\tvalid_1's multi_logloss: 0.262111\n",
      "[2500]\ttraining's multi_logloss: 0.25694\tvalid_1's multi_logloss: 0.262105\n",
      "[2600]\ttraining's multi_logloss: 0.256806\tvalid_1's multi_logloss: 0.262094\n",
      "[2700]\ttraining's multi_logloss: 0.256669\tvalid_1's multi_logloss: 0.262087\n",
      "[2800]\ttraining's multi_logloss: 0.256527\tvalid_1's multi_logloss: 0.262091\n",
      "[2900]\ttraining's multi_logloss: 0.256396\tvalid_1's multi_logloss: 0.262086\n",
      "[3000]\ttraining's multi_logloss: 0.256264\tvalid_1's multi_logloss: 0.262088\n",
      "[3100]\ttraining's multi_logloss: 0.256135\tvalid_1's multi_logloss: 0.262092\n",
      "[3200]\ttraining's multi_logloss: 0.256007\tvalid_1's multi_logloss: 0.262111\n",
      "[3300]\ttraining's multi_logloss: 0.255879\tvalid_1's multi_logloss: 0.262115\n",
      "[3400]\ttraining's multi_logloss: 0.255754\tvalid_1's multi_logloss: 0.26212\n",
      "[3500]\ttraining's multi_logloss: 0.255634\tvalid_1's multi_logloss: 0.262112\n",
      "[3600]\ttraining's multi_logloss: 0.255515\tvalid_1's multi_logloss: 0.262112\n",
      "[3700]\ttraining's multi_logloss: 0.255392\tvalid_1's multi_logloss: 0.262119\n",
      "[3800]\ttraining's multi_logloss: 0.255277\tvalid_1's multi_logloss: 0.262135\n",
      "[3900]\ttraining's multi_logloss: 0.255159\tvalid_1's multi_logloss: 0.262137\n",
      "Early stopping, best iteration is:\n",
      "[2932]\ttraining's multi_logloss: 0.256354\tvalid_1's multi_logloss: 0.262079\n"
     ]
    },
    {
     "data": {
      "image/png": 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1W5sJsH7LpS/QswiuYxhGANm8eTPTpk1j7dq1lCpVih49etCrVy8a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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a978de0f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 7 log loss 0.26207906849272916 in 2932\n",
      "Fold :  8\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.532684\tvalid_1's multi_logloss: 0.534602\n",
      "[200]\ttraining's multi_logloss: 0.365127\tvalid_1's multi_logloss: 0.367013\n",
      "[300]\ttraining's multi_logloss: 0.305249\tvalid_1's multi_logloss: 0.306962\n",
      "[400]\ttraining's multi_logloss: 0.281322\tvalid_1's multi_logloss: 0.283016\n",
      "[500]\ttraining's multi_logloss: 0.270818\tvalid_1's multi_logloss: 0.272704\n",
      "[600]\ttraining's multi_logloss: 0.265755\tvalid_1's multi_logloss: 0.267835\n",
      "[700]\ttraining's multi_logloss: 0.263083\tvalid_1's multi_logloss: 0.265346\n",
      "[800]\ttraining's multi_logloss: 0.261553\tvalid_1's multi_logloss: 0.263989\n",
      "[900]\ttraining's multi_logloss: 0.260594\tvalid_1's multi_logloss: 0.263228\n",
      "[1000]\ttraining's multi_logloss: 0.259971\tvalid_1's multi_logloss: 0.262784\n",
      "[1100]\ttraining's multi_logloss: 0.259532\tvalid_1's multi_logloss: 0.262532\n",
      "[1200]\ttraining's multi_logloss: 0.259214\tvalid_1's multi_logloss: 0.262384\n",
      "[1300]\ttraining's multi_logloss: 0.258965\tvalid_1's multi_logloss: 0.262309\n",
      "[1400]\ttraining's multi_logloss: 0.258757\tvalid_1's multi_logloss: 0.262267\n",
      "[1500]\ttraining's multi_logloss: 0.258572\tvalid_1's multi_logloss: 0.262245\n",
      "[1600]\ttraining's multi_logloss: 0.258397\tvalid_1's multi_logloss: 0.262202\n",
      "[1700]\ttraining's multi_logloss: 0.258231\tvalid_1's multi_logloss: 0.262183\n",
      "[1800]\ttraining's multi_logloss: 0.258063\tvalid_1's multi_logloss: 0.262162\n",
      "[1900]\ttraining's multi_logloss: 0.257905\tvalid_1's multi_logloss: 0.262155\n",
      "[2000]\ttraining's multi_logloss: 0.257747\tvalid_1's multi_logloss: 0.262149\n",
      "[2100]\ttraining's multi_logloss: 0.257598\tvalid_1's multi_logloss: 0.262147\n",
      "[2200]\ttraining's multi_logloss: 0.257455\tvalid_1's multi_logloss: 0.262146\n",
      "[2300]\ttraining's multi_logloss: 0.257312\tvalid_1's multi_logloss: 0.262155\n",
      "[2400]\ttraining's multi_logloss: 0.257168\tvalid_1's multi_logloss: 0.262161\n",
      "[2500]\ttraining's multi_logloss: 0.257022\tvalid_1's multi_logloss: 0.262162\n",
      "[2600]\ttraining's multi_logloss: 0.256895\tvalid_1's multi_logloss: 0.262167\n",
      "[2700]\ttraining's multi_logloss: 0.256766\tvalid_1's multi_logloss: 0.262168\n",
      "[2800]\ttraining's multi_logloss: 0.256633\tvalid_1's multi_logloss: 0.262178\n",
      "[2900]\ttraining's multi_logloss: 0.256512\tvalid_1's multi_logloss: 0.262198\n",
      "[3000]\ttraining's multi_logloss: 0.256384\tvalid_1's multi_logloss: 0.262204\n",
      "Early stopping, best iteration is:\n",
      "[2052]\ttraining's multi_logloss: 0.25767\tvalid_1's multi_logloss: 0.262138\n"
     ]
    },
    {
     "data": {
      "image/png": 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o2bMnixYtAiApKYlBgwYBMHDgQJKSkgBYtGgRN9xwQ6UMaobSYdL9LbsmYWnU\nxgTyPgaDITCkpqYyZswYIiIiOHr0KBdffDGNGzemc+fO/OMf/+D48eOsXLmSp556is6dO9OqVSs6\nd+5Mbm4uhw4dYsWKFTRv3pyFCxcG2xVDORDodP9KX55GRKKAp7Ge0W0RkRQRGWOfu1pEMoDfAH+3\nfTAYDJWMmJgY5s6dS0pKCgcPHqRRo0Zs2LCBzMxMXnzxRVJTU3n55Ze54447SE5OplmzZqxYsYKd\nO3eybt06ADZu3Gh2FXEIgX7Gds5+gbUEKYCo6jisWZpP7KBY7uVpihgvQ0QmY5WtaaEFy9bEYyWb\nuHf9n13UOAaDoXKwatUqLr/8clq1akVqairdu3cHoHfv3vTt25fnnnuOTp06ea6Pj48nNzeXvLw8\natUqnFtmCEUCrWNbipXBeLmqxmGl5jcJ1D0vgBUUvdz5vqom2q+5FWmUwWAoPQsXLmT48OEAJCQk\nsHy5lSidnJxMenr6edcvXryYTp06maDmICRQYkQRuQGYqKrdC7ULlu6sgI7NLjT6kaomBErHJiJf\nUHy6f7b3jK2wHcX46l22pvOEGXP8/6BCjCbhkHkq2FYEDif751TfOjS3UvOzs7OpVasWQ4cO5e23\n36Zhw4Z89913zJo1ixMnTtCtWzeWLFnCsmXLPH3379/PM888w4svvkjz5s2D5YJfZGdnExERUfKF\nIUrPnj03q+pV5TJYeZUJKPzCGWVrRmFV0N6GpYErcQxTtia0cbJ/TvZN1fLvww8/1N69e/s8n5qa\nqldffbXnOD09Xdu2bav//e9/K8rEC8Lp3x+mbE2Fla1ZAbRW1SuAz7AE4AaDoZKyYMECzzIkWGJs\ngHPnzjFp0iTuv/9+AI4fP86AAQP461//Srdu3YJiqyFwGB1bMajqUVV1jzMH3/4YDIZKQG5uLitX\nruTWW2/1tC1YsIB27doRGxtLs2bNGD16NACzZ8/m66+/5rnnniMxMZHExERPEDSEPoEMbKuBWiJy\nj7uhkI6tuog0wtKxbSzUNw1IFJFqItKCIJWtEZGmXocDgd3BsMNgMFikpqZ6AlFiYiJ169ZlxowZ\n3H777fz+97+nRYsWdOzYkcTERADi4uK4+OKLqVWrFitXrmTNmjUAPPPMM5w8eZKUlBTPq3HjxsF0\nzVCOBCzdX1VVRAYDM0RkPJCLFbAeASKwdGyKrWOzk0fceOvYdhBAHRuAiLwI/BaobevW5qolJfiD\niAzEmkH+hPXMzWAwBImYmBhSUlIAOHv2LM2bN2fw4ME88sgjni2nHnvsMc9ej5GRkaxYsYJmzZqx\nY8cO+vbt69kA2eBcAqpjU9VEq3zpAAAgAElEQVRDwG2F20XkJazK1FcDz4rISKyMxgS7n0fH5pUt\n6bLPlauOzT7/J8DX7v+LgJuxNl7+D1Z9NoPBUAnw1qu5UVU++OADVq9eDWD0alWUCt8E2UvflqSq\nw+y2RKwMyb0VbU8JvI6Vxr8B+AToB/yzuA6mbE1o42T/Qt23tCkDChx769XcrFu3jiZNmtC2bdvz\n+hu9WtUhYDq2Im8YXH3bN8AlWM8WawHHgRvV1rEVsqcpsEZVY+3j4ViSg/t8XGt0bA7Byf6Fum9u\nvRrAmTNnCujVwNJ5zZkzh+bNm3PbbQUXikJJr1YURsdWCspLN+DvixDRtwFXAZ95HV+HFWCNjs3B\nONk/J/nmS6/22WefaePGjTU9Pb1Ae6jp1YrCSd+fLwhxHVtRVDZ9my9ZgqkZbzBUAgrr1QA2b95M\nbGwsUVFRnjajV6uaBCOwhYq+LQOI8jqOAg75YaPBYAggOTk55+nVAFavXn1esDN6tapJMAJbSOjb\nVPUwkCUiv7Kf/40AlpXQzWAwFENROrTk5GTi4+OpVq0amzZt8ly/cuVKOnfuTIcOHejcuTOrV6+m\ndu3aHD161JPS72b8+PGenUXcGL1a1aTCsyJVQ0ffBjyAJRsIx8qGLDYj0mAwFE9ROrScnByWLFnC\nffcVzM0yOjRDWQhoYBORS4EZWM/K8rADmKrupZC+zQ5gN6mtZXOjqmnAefq2wmgZ9W0iUltEPsaq\nAnAWWKGq4+3zm4AEERkKJNt9N2EwGC4YXzq0whgdmqEsBCywhZhebZqqrhGRmsAqEblJVf8JICIX\nY2VyfuHPQEbHFto42b9g++aPDq04jA7N4C+BnLH1xEro+Ju7QVVTxGIqhfRq3h0DVY/Nl5GqmiMi\nU0TE/a+lOfB3ERmglr7tOXv8x4tytJCOjQkd8v3+kEKNJuHWD6RTcbJ/wfbN5XJ53p85c4bFixdz\n8803F2g/fvw4mzdvJjs7u0Bfbx2a9/XeZGdnF3nOCTjdv3KlvHQDhV+EiF6tkG317X7R9nEnYLH9\n3oUVbI2OzcE42b/K5FtRddOuv/56/fLLLwu0+atDq0z+BQKn+0c56tgqPHkEL70akCkibr3aNj/7\nF9ar5anqGRHxqVcDEBG3Xu38uvA2IhIGLABeUdVvRaQaMB2z8bHBUO740qH5wujQDGXB1GP7hTeA\nfao6wz6+GCtpxSUiacCvgOUiUj5bvhgMVRRfOrSlS5cSFRXF+vXrGTBgAH379gWMDs1QNkw9Nsuu\nSVjLlo+429TanSRSVVuramusjZAHqpUpaTAYykBqair/7//9P1q0aMH111/v0bHl5+dTr149zpw5\nw8cff8ynn34KQJcuXYiNjeXs2bNUr16dl19+2ejQDCVS5euxiUgU8DSwB9hiJXMyW1XnBuqeBkNV\nxejYDBVBla/HpqoZFLE8KiIurMQW957o3xU1jsFgKB1Gx2YIFKYeW8ncUZrlR6NjC22c7F+wfTM6\nNkNFEYysyGDq2/yux1YajI7NOTjZv2D7ZnRsF4bT/StXyks34O+LENK3YWnXtgMpwJ+xC7MW9zI6\nttDGyf5VJt+Mjq30ON0/TD02n5R3PTawliE7YBUZvQ64sxT2GAyGIjA6NkMgMfXYilmOVdWD9p9Z\nwHsEWHZgMDiR48ePM3ToUGJjY2nfvj1r1qzhX//6F6+99hodOnTglltuYf78+R4d23XXXcfFF19M\n+/bt+c1vfmN0bIZSU+rAJiINROSKC7hnSOjbRCRMRCLt9zWAm7GkBwaDoRSMHTuWfv36sWfPHrZu\n3UqnTp1o27Yt06ZNY/v27QwePJg9e/aQkZHB22+/zaBBg8jKymLz5s3s27ePnTt3mnpqhlLhV2AT\nEZeI1BWRhlj6s7dF5OWy3NCeVd0L/FlEzohILvAx8D+sbbW2YgW/P6nq91iVq9vY3b31bdMIbD22\nWsB2ETmNpcE7CMwJ4P0MBsfx888/s3btWu6++24AatasSf369UlNTaV79+4A9O7dm8WLFwMgIpw8\neZL8/HxOnTpFzZo1qVu3btDsN4Qm/s7Y6qnqz8CtwNuq2hkr+7DU2On+fwMmq2oNVb0I6APkquo4\nVU1Q1Q76S0ZkBvA1WEFRVe9Q1XhVvV1Ve2gR+jZVneZ17NG3qZ1RaR/f7O5fGFU9CQzGegZ3SlXH\nqrW/pcFg8JNvv/2WRo0aMXr0aDp16sSYMWM4efIkCQkJLF++HIDk5GTS061tXIcOHUqdOnVo2rQp\nLVu25PHHH6dhw4bBdMEQgvib7h8mIk2xxNZPX+A9Q6KcjW3XBvs+fjtndGyhjZP9q2jf0qYMID8/\nny1btjBr1iy6dOnC2LFjmTJlCm+99RZ/+MMf+L//+z8GDhxIzZo1Adi4cSPVq1fn0KFDHDt2jOuu\nu44bb7yR6OjoCrPbEPr4G9j+D/gU+FxVvxSRaGBfGe+ZAGz20X4rkIiVnh8JfCkia0sxbh3ApapP\niMhSYBLQG4gDkoDl9nWJWOVo8oBUEZkFLMJaevTmTvVT3+atY2vUqBEf9KtTCrNDi+zsbOYZ/0KS\nivbN5XLx008/ERkZyalTp3C5XFx++eW899579OrVi6eeegqA9PR0GjdujMvlYsaMGcTFxfH5558D\nEB0dTVJSEj179izxfk7XeTndv/LEr8CmqslAstfxt1i6s/IkaOVsVLXLhRiuqm9gVQcgJiZGe/To\ncSHDVWpcLhfGv9AkWL5Nnz6dpk2bEhMTg8vl4rrrriMuLo7GjRtz7tw5Ro0axbhx4+jRowdffPEF\ne/bs4frrrycnJ4cDBw7wwgsvcMUVJeerOfm7A+f7V574mzzSTkRWicgO+/gKEXmmjPcMmXR/g8Fw\n4cyaNYs77riDK664gpSUFJ566ikWLFhAu3btiI2NpVmzZowePRqAhx56iOzsbBISErj66qsZPXq0\nX0HNYPDG3x/1OcA44O8AqrpNRN7DWu4rLauBySJyj6rOgfPS/ZOAhljp/uMoGLzSgAftIqDNMboy\ng6HScvz4ccaMGcOOHTsQEd566y1q165N//79yc7OJiYmhvnz51O3bl1WrlzJ+PHjOX36NDVr1mTW\nrFnccMMNwXbBEKL4mxVZW1ULa8rKtOmcPasaDPQWkW9EZCcwEUsA7Svd35uKTPdHRF4UkQygtohk\niMjEQN7PYHAShfVr7du3Z8yYMUyZMsWjX5s6dSrwS3ma7du3k5SUxJ13mk1+DGXH3xnbjyJyOVa2\nIiIyFDh8Afc9Z7/AWoIUrH0Yx2HN0nxiB8U7ijhXruVsRKQ2EA9kA7uBFYXHNRgMvnHr1+bNmwdY\n+rWaNWuep1/r27cvzz33nClPYyhX/J2xPYS1DBkrIgexioXeX5YbepWtcanq5aoah5Wa36Qs4wWY\naaoai5VF2U1Ebgq2QQZDKFBa/Zo3pjyN4UKRX/ItirjAep41VFU/EJE6QDV778Sy3VDkBmCiqnYv\n1C5YurMCOjavQqMJgdKxicgXlJDuLyIzsaoMnLf7SKGyNZ0nzHDuBiVNwiHzVMnXhSpO9q+ifOvQ\nvB6pqak8+OCDzJo1i7i4OGbNmkWdOnW48cYbmTVrFidOnKBbt24sWbKEZcuWefp6l6dp3rx5qe6b\nnZ1NREREyReGKE73r2fPnptV9apyGcyfEgDA2vIqJ0AIla3xuk99u190SdeasjWhjZP9q0jfDh8+\nrK1atfIcr127Vvv371/gmtTUVL366qs9x/6WpykKJ393qs73jyCUrVkpIo+LSAsRaeh+lTqKFk9l\nLFuDiIQBC4BX1NLvGQyGErj00ktp0aIFqampAKxatYq4uDjPzvznzp1j0qRJ3H+/9UTDlKcxlCf+\nBra7sJ6zrcXaNWQzsKmM9ww1HdsbwD5VneGHfQaDwaY0+rXZs2eb8jSGcsOvwKaql/l4lXXztpAo\nW2PbNQlr2fKRQN7HYAhVCtdaW79+PWAFtdtvv51Tp07Rr18/PvzwQyIiItiyZQu1atWievXq1KtX\nz7MP6zPPPMPJkydNeRpDueBXur+IjPDVrqrvlPaGqqoiMhiYISLjsUrCpGEFjwgsHZti69js5BE3\n3jq2HQRQxyYiUVgbPu8Bttj/AGer6txA3dNgCDXcWrVFixZx+vRpcnJyWLNmDcuWLWPbtm3UqlXL\nM/NKTk4mLy+P7du3k5OTQ1xcHMOHD6d169bBdcLgOPzVsXk/67oI6IUVVIoNbCJyKTDD7p+HHcBU\ndS9WpQDva1tjJX8keLerahrWxskB07GJyPPACKCB17UZtk1vAY2An/jlGZ7BUOUpSqv2+uuvM378\neE+6vnvmZWqtGSoKfzdBftj7WETqAe8W18dLr5akqsPstkSsjMe9ZbI2cKwAZnN+xYJpwDuqmmTL\nFP4KFLslgilbE9o42b/y9C1tyoACWrWtW7fSuXNnZs6cyd69e1m3bh1PP/00F110EdOmTePqq69m\n6NChLFu2jKZNm5KTk8P06dNNrTVDQCjrBsA5QNsSrgmZumvATCwdW7iIpNhtd2KVvHnUPl4DfOir\ncyEdGxM6lGm3sZCgSbj1A+lUnOxfefrmcrlITU1l8+bNjBo1ilGjRjFr1iweeOABTpw4wfbt25ky\nZQp79uxh4MCBvPfee+zYsYMff/yRBQsWkJWVxdixY4mIiKBZs2blYpPTy7o43b9yxR9NANaMZrn9\n+ghL0/VCCX1CUa+WXej4PWCs/f5W+96XFDeG0bGFNk72r7x9K0qr1rdv3wL3io6O1iNHjuiDDz6o\n77zzjqd99OjR+v7775ebPU7+7lSd7x9B0LFNA16yX38FuqvqE372LUyl1KsVwePA9SLyFXA9cJAy\nbv5sMDiNorRqv/71r1m9ejUAe/fu5fTp00RGRtKyZUtWr16NqnLy5Ek2bNhAbGxsMF0wOBR/lyL7\nFw5kIvJCCcFtJzDUR3vA9Gq2mNrNBdddU9VDWDM1RCQCGKJ2kVKDwfCLVu306dNER0fz9ttvU6dO\nHe666y4SEhKoWbMmSUlJiAgPPfQQo0ePJiEhAVU1tdYMAcPfGVtvH20lbQgcMnq1ohCRSHuvTIAn\nsTIkDYYqiS/NWmJiIps2bWLEiBEsW7aMs2fPeuqptW3blvz8fMaPH8+OHTuIiIggOTmZnTt3smvX\nLsaNK7KQh8FwQRQb2ETkARHZDsSIyDav136s2mlFYs+qQr3uWg8gVUT2Yj0PfD6QdhgMlRlf9dUA\n0tPTWblyJS1btvRcO3nyZBITE9m2bRvvvPMOY8eODZbZhipISctz7wH/xHquNt6rPUtVfyppcHsp\n77bC7SLyEtAC69nasyIyEiuj8Ty9mtfu/i77XLnWXbPP/wnwlTVZHThl//mTqub5uMZgcDxFadYA\nHn30UV588UUGDRrkuX7Xrl08+eSTAMTGxpKWlkZmZiZNmlTG6lQGp1FsYLOfJ50AhgOISGOs510R\nIhKhqt+V9oahom8TkUuAqUBnVf1BRJJEpJeqriqun9GxhTZO9q+svhWnWVu1ahXNmzenY8eOBfp0\n7NiRJUuWcO2117Jx40YOHDhARkaGCWyGCsHfLbVuAV4GmgFHsDIMd2NVmC4twdS3fQNcgrUEWws4\nDtyoXnXXvIgG9qrqD/bxZ1hShfMCm9GxOQcn+1dW34rSrN19991s3bqVqVOn4nK5yM3N5fPPP6de\nvXp069aN2bNn06ZNG6Kjo2nTpg1fffUVWVllLuVYIk7XeTndv3LFH00A1vOwS4Cv7OOewBtl0RcQ\nIvo2oAGQYd8/DFgMrCjJP6NjC22c7N+F+OZLs3bDDTdoo0aNtFWrVtqqVSutXr26tmjRQg8fPlyg\n77lz57RVq1Z64sSJMt/fH5z83ak63z+CoGM7o6pHgWoiUk1V1wCJfvb1l0qlb1PVY8ADwPvAOqxM\nTWf+V95gKAFfmrUrr7ySI0eOkJaWRlpaGlFRUWzZsoVLL72U48ePc/r0aQDmzp1L9+7dzb6QhgrD\nX23XcVvHtQ6YLyJHKPuPfMjo21R1BdauK+7lxrN+2GgwOJLJkyfzq1/9ipycHGrWrMmiRYs856ZN\nm8aBAwc4evQokZGRfPHFFwwZMoT8/HzCw8P56KOPgmi5oarh74xtENb+kI9gzYq+AW4p4z1DRt9m\nJ8sgIg2ABwFTssZQZZk7dy5Tp04lLy+Po0eP0qVLF6Bguv8ll1wCwGeffca4cePIzc1l/fr1TJgw\nIZimG6oY/hYaPYmVnt9DVZOwfuBPl+WG9qzqXuDPInJGRHKBj4H/4VvfFgW0sbtXqL4NSLHtOwz8\ngBXQDYYqhzvd/+677wasdP/69esDv6T7u4uGgpXu36tXL6Bgur/BUBH4Fdjs2dUi4O92U3OK2One\nj7EE+BswWVVrqOpFQB8gV1XHqWqCqnbQXzIiM4CvwQqKqnqHqsar6u2q2kOL0Lep6jSvY4++Te2M\nSvv4Znf/Ioi17QsHfgZ+UxafDYZQxzvdv1OnTowZM4aTJ0+yfPnyYtP9gQLp/gZDReDvM7aHsJb9\nvgBQ1X3uZboyEDLlbFT1Z/ttGFDTtqtYjI4ttHGyfxeiY8vPz2fLli3MmjWLLl26MHbsWCZOnMja\ntWv597//fV6f8ePHM3bsWBITE+nQoQOdOnUiLKysVbIMhtLh79+0PFU97V5qsJMxSvyRL4IEYLOP\n9luxMi07ApHAlyKythTj1gFcqvqEiCwFJmHtcRkHJGGV3MG+RyesJJJUEZmFNRutVWi8O1V1u4h8\nihXU/2lfdx7eOrZGjRrxQb86pTA7tMjOzmae8S8kKatvLpeLn376icjISE6dOoXL5eLyyy9n3rx5\n7N+/n5iYGAB++OEH4uPjef3112nYsCEjR45k5MiRqCrDhw8nIyODY8eOlbdbHpyu83K6f+WJv4Ht\nPyLyFFYhzt5YiRQrytkWT7o/kCki7nT/Yvek9KJwun+eqp6x97ps7XXdKrV36BeRXUArVe1S1KCq\n2ldELgLmAzdgae0KX/MG8AZATEyM9ujRw0+TQw+Xy4XxLzS5UN+mT59O06ZNiYmJweVy0atXL6ZO\nneo537p1azZt2kRkZCTHjx+ndu3a1KxZkzlz5tCnTx8GDBhQDl4UjZO/O3C+f+WJv4FtPHA3VsC4\nD/iEsmcIhky6vxtVzRWR5VjZoecFNoOhKuCrRE1R7N69mxEjRlC9enXi4uJ48803K9BSQ1WnpN39\nW4IVHFR1jqr+RlWH2u/LuhQZEun+IhIhIk3t92FAf2BPoO5nMAQDX6VokpOTiY+Pp1q1amzatMlz\nbWJiIkOGDCEnJ4fdu3ezcWPBf55paWlERkYC0LVrV/bt28eePXtYsmQJDRo0qFC/DFWbkmYrHwJX\nAojIYlUdcqE3VFUVkcHADBEZD+RiBaxHgAisdH/FTve3d/d3453uv4PApvvXAZaLSC2sbb5WY2Vz\nGgyOwV2KZtGiRZw+fZqcnBzq16/PkiVLuO+++wpcu2vXLhYuXMjOnTs5dOgQN954I3v37qV69epB\nst5g8E1Jgc17eTC6tIOLyKXADKxnZXnYAUxV91KonI0dwG5Su3SNG1VNw0o4KVDOpjCF0/19nVMf\n5WxEpLaIfIyVJXkWaz/I8aqaKSJvYmWEnsFKOGmHtQ2XwRDyFFWKxq1PK8yyZcsYNmwYtWrV4rLL\nLqNNmzZs3LiRrl27VqDVBkPJlKRj0yLel4hXeRqXql6uqnFYKfiVsW7FNFWNxQpe3UTEXR38PVtT\nl4glHXg5aBYaDOVMUdq0ojh48CAtWrTwHEdFRXHw4MGKMNVgKBUlzdg6isjPWDO3cPs99rGqanG7\nmoaEXk1Vc0Rkir3kCJb4/O8iMkALlrOpQxHBvXDZmlnzlxXzsYQ2TcIx/oUo3r51aF7PZymaBx54\ngLvuuguwnr9t3ryZ7OxsADIyMti9e7cn5fzw4cPs3LnT81wt2Dg9Hd7p/pUr5VUmoPCLEClPU8i2\n+na/aK+2h7C20koH2pY0hilbE9o42b/CvvkqRdO/f3/P8fXXX69ffvml53jy5Mk6efJkz3GfPn30\nf//7X8DsLS1O/u5Une8fQShbU55UqvI0buzMxwXAK6r6rbtdVV9V1cuBJ4BnSmGnwVCp8VWKJi4u\nrsjrBw4cyMKFC8nLy2P//v3s27ePa64J6D7kBkOZCOQeN6GmV3sD2KeqM4o4vxB4vSTDDYZQwpc2\nbenSpTz88MP88MMPDBgwgMTERD799FPi4+O57bbbiIuLIywsjFdffdVkRBoqJYEMbKuBySJyj6rO\ngfP0aklAQyy92jgKBq804EERqYb1zCvQ5WkmYS1bjinU3lZV99mHA4B9hfsaDKHI8ePHGTNmDDt2\n7EBEeOutt4iJieG2224jLS2N9u3bs337dho0aMCePXvo2rUrW7Zs4fnnn+ebb0yRC0PlJmBLkfas\najDQW0S+EZGdwETgPXyXp/GmwsrTiEgU8DTWnpJbRCRFRNwB7vcislNEUoA/AiMDZYfBUJG49Wt7\n9uxh69attG/fnilTptCrVy/27dtHr169mDJlCgANGzbklVde4fHHHw+y1QaDfwR6u+1z9gusJUgB\nRFXHYc3SfGIHxXLVqxVzrwwRmQyMwEow8R5/LICIDAWSscrXGAwhTVH6tWXLlnmy7kaOHEmPHj14\n4YUXaNy4MY0bN+bjj51Z9cDgPAIW2Lx0bEmqOsxuS8TKhNwbqPuWkRXAbHwsNYrIxVgZnl/4M5Ap\nWxPaONm/xzrk04OC+rWtW7fSuXNnZs6cSWZmJk2bNgWgadOmHDlyJKj2GgxlJZAztkqnYxORL/Bd\nnmaDfR9ffjxnj1/kOkxhHduEDvklfTYhS5Nw6wfSqTjZvybh1g7xRenX8vPzC+ikCh+npaURHh5e\nabVUTtd5Od2/cqW8dAOFX4Smji270HEnYLH93oUVbI2OzcE42T+3b0Xp19q1a6eHDh1SVdVDhw5p\n4b/Lzz77rE6dOrWizC01Tv7uVJ3vH0bHVv46tsLYGZnTgcdK089gqOwUpV8bOHAgSUlJACQlJTFo\n0KBgmmkwlBmjYyuai7E2X3bZS5SXYu32P1BVNxXb02Co5PjSr507d47bbruNN998k5YtW5KcnAzA\n999/z1VXXcXPP/9MtWrVmDFjBrt27aJu3eJ21DMYgkcgZ2whUXetKOzZXqSqtlbV1sAGwAQ1Q8jT\nunVr7rzzTvLz86lZsyYffvgh3333HTfffDNHjhwhNjaWpUuX0rBhQ+bPn0+/fv2IjIwkOjqan3/+\nmY8++sgENUOlpsrr2ABE5EURyQBqi0iGiEwM5P0MhmCzZs0aUlJSPIVEx4wZw5QpU9i+fTuDBw9m\n6tSpANxxxx2kpKSQkpLCu+++S+vWrUlMTAym6QZDiQRUx6aqhyhUdw1ARF4CWmA9W3tWREZiZTSe\nV3fNrtP2kaq67HPlqmOzz/8JKLD7v53mv86rKQr4HWBmbAbHkZqaSvfu3QHo3bs3ffv25bnnnitw\nzYIFCxg+fHgwzDMYSkWgBdrnESr6NlXNAjz/NRWRzcCSkvoZHVto41T/0qYM8LwXEfr06YOIcN99\n93HvvfeSkJDA8uXLGTRoEMnJyaSnp583xvvvv8+yZc4s6WNwFvJLHkYF3VDkBmCiqnYv1C5YerEC\n+javGVtCOejbvgEuwVqCrQUcB27UgnXXfNncFmvZtKX6+MAK6dg6T5gxp/QfTIjQJBwyTwXbisDh\nVP86NK9HdnY2ERER/Pjjj0RGRnLs2DEef/xx/vCHP9CgQQNmzZrFiRMn6NatG0uWLCkQxHbt2sW0\nadN46623guhF8bj9cypO969nz56bVfWqchmsvHQD/r4ITX3bBKwAWqJ/RscW2jjZP1+++dKmpaam\n6tVXX12g7ZFHHtHnn38+kOZdME7+7lSd7x8hrmMrisqsbxuGVavNYAhpTp48SVZWluf9v//9bxIS\nEjzbZ507d45JkyZx//33e/qcO3eO5ORkhg0bFhSbDYbSEozAthPo7KM9YPo2Cj5LLJW+TUQ6AmGq\nutkP+wyGSk1mZibXXnstHTt25JprrmHAgAH069ePBQsW0K5dO2JjY2nWrBmjR4/29Fm7di1RUVFE\nR0cH0XKDwX8qPHmEEKrTZjMcM1szOIBhw4bRqFEjqlevTlhYGFu3bmXr1q107dqV7OxsYmJimD9/\nPnXr1uXo0aMMHTqUL7/8klGjRrFhw4Zgm28w+E1AZ2wicqmILLR1bLtE5BOgLb71bf8BriQI+jYR\neV5E0u0EFO/26cCjwG0isldEjpfH/QyGYOGvfu2iiy7iueeeY9q0acE012AoE0EpW6Oqeymkb7Oz\nHzPV1rK5UdU0rK2tCujbCqMXoG8TkVx8lK1R1UexAhsi8jDWpsgGg2MoSr9Wp04drr32Wr7++usg\nW2gwlJ4qVbamKEO1+LI1boYDz5bktNGxhTZO9M+tYSurfs1gCDUCGdgSAF8JF7diCZ87ApHAlyKy\nthTj1gFcqvqEiCwFJgG9gTggCVhuX5eINcPKA1JFZJaq+vxX61WnLVxEUuzmO9XWt4lIK+AyrCVS\nX/09OrZGjRrxQb86pXAntMjOzmae8S+kcNfwmjJlCq1atfLo106dOsX999/PpEmTGDduHN26daNa\ntWoFan7t2bOHgwcPhkQdMKfXK3O6f+VJMJJHPGn9QKaIuNP6t/nZv3Baf56qnhERn2n9ACLiTuv3\nGdhUtYt9Xbaq+toIbxiwyLbZV/83gDcAYmJitEePHn66Enq4XC6Mf6GJt29bt27lzJkzjBgxghEj\nRgCwd+9edu7cWcD/tLQ0srOzQ+IzcfJ3B873rzwJZPJISKX1l4DRsRlCmpMnT5KTk+N5749+zWAI\nVUzZmhIQkRigAbA+WDYYDBdKZmYmgwYNIjw8nEsuuYQDBw7Qr18/pk2bRnh4OOHh4fzvf/9jyJAh\nnj4NGjTg7rvv5rXXXjxQZw4AABnhSURBVKNRo0bs2rUriB4YDP5jytZQYtma4cBCr1miwRByREdH\nc8kll5Cenk5ubi4HDx4ErPT/f/3rX+Tl5fHUU0950vt37dpFy5YtycnJ4ZtvvqFu3brExMQE0wWD\nwW8C/YztnP0CawlSsDZeHoclvvZJoNL6i+EMVoZmjqpGuRtFpDtWduUVIrJJVReVMI7BEFIUle6/\nbNkyhg0bRq1atbjsssto06YNGzdupGvXrkG22GAomYDN2Lx0bC5VvVxV47BS85sE6p4XwAp8L3d+\nh7Vx8nsVao3BEADc6f6dO3fmjTfeAPCk+wMF0v0PHjxIixYtPH2joqI8szyDobJTpXRsXmn93txZ\nlI7NFocjIucohkJla5g137k1q5qEY/wLMTo0rweULt0/IyOD3bt3e9LLDx8+zM6dO4mMjAyiJ8Xj\n9HR4p/tXnlQ1HVuXMnlSAoXT/R++Y1AgblMpcLlc3ObglGMn+1eadP/1661cKff1f/3rX+nTp0+l\nXop0ejq80/0rT4Kxu39lLk9jMDiS0qb7Dxw4kIULF5KXl8f+/fvZt28f11wTtORkg6FUGB2bwVAF\nyMzM5OGHH/a7XE18fDy33XYbcXFx9OvXj1dffZXq1asH2QuDwT+Mjs1gCEFat25Nhw4dSExM5Kqr\nripwbtq0aYgIP/74o6ctOjqacePGsWPHDv7yl7/w9NNPAzB27Fj27t3L3r17mTJlSoHnzE8//TTf\nfPMNqamp3HTTTRXjmMFQDgRsFqOqKiKDgRkiMh7IxQpYjwARWDo2xdax2bv7u/HWse2gAnRswG+x\ndWzAXFWdaAfipVgC7VtE5C+qGh9IWwwGf1mzZs15yRzp6emsXLmSli1bFmg/e/Ysb7zxBn379q1I\nEw2GoGB0bBY+dWzAdcDPwE/AD8BdJYxjMASVRx99lBdffJFBgwomMM2aNYvrrruOEydOBMkyg6Hi\nCEo9NmBvoO5bRlbgox4b8BWW7CBHRB7AkhXcXtxApmxNaFPZ/SuuBM3y5ctp3rw5HTt2LNDn4MGD\nLF26lAkTJvDuu+8Gw2yDoUIxOrbidWxrvA43AL/z5WhhHduEDvl+fDyhSZNw68ffqVR2/9w6pqlT\npxIZGVlAk/a3v/2NqVOn4nK5yM3N5fPPP6devXpMnDiR22+/nVOnTvH9999Xej1aWXG6zsvp/pUr\nqhqQF/AHYLqP9iHASqA61uztO6Ap/7+9c4+uqrrz+OcHVJCHoIIMGlYAEREYHqWLouJj0CoQJmpL\nWVRmRKvV8YU6a1p1RW2lXdKqYxWh1RY1vioUeamVSnnEB0WUCEEQMKmkmsAgRBESFEL4zR9735uT\n5ObJvbm5J7/PWmfdffbe55zfLzu5O+ec33f/XKj+Zt/namB24JjXgAt9WYHxvrwYWA58C6eL2xg4\n/hOgKy6i8p9A7wbYXFpH22zgnvrOMWDAAA0zq1evTrYJCSUV/fv5z3+uM2bM0B49emh6erqmp6dr\n27ZttXfv3rpr1y7t06ePpqena8+ePbVTp07ao0cPXbx4cbLNjjupOHaNIez+Aes1TvOP5WNrACLy\nH8B3gAuacrxhxJOysjKOHj1Kly5dopq0++67L6pJAxc1uX79erp3786OHTsAd7eXnZ3NxIkTufzy\ny5NlvmEknERObFuASTHqE6ZjE5G469hE5GIgC7hAVQ/V198wEs3u3bu54oorADhy5AhXXnkl48aN\nS7JVhtFyMB1bHYjICOBJIFNVP6+vv2HEg1gatQULFjB48GDatGnDF198QV5eHnl5ebz11lusWLGC\nzp07c8stt0TPUVhYGPM9WnZ2NpMmxfp/0zDCg+nYqF3HBjzkbV3gA0s+VdXMRNpiGFBTozZkyBAW\nLVrEDTfcUKVfhw4d+OUvf8nmzZvZvHlzc5tpGC2SVq9jE5GOwGCgFNgKvBo4bwbwHG5psBJcQIxh\nNDtnnXVWzPpOnToxZswYCgoKmtkiw2i5mI7N8bCqrhaR44CVIjJeVZcB1wJfqmp/EZkC/AbTsbVo\nndexkkz/6tKoGYbRcEzH5nRsq719h0XkAyCy+shlwC98+WVgtohIIHglYq/p2EJCMv2rS6MWEV3v\n27eP3NxcSktLqxy7bds2iouL69Q5hV0HZf4ZESwfWwAR6Qb8O/CYrzoNLxFQ1SMi8hVwMrA3eJxa\nPrbQ0NL8i+RNi+Th6tatGyNHjqyx8HFhYSGlpaV15usKez4v88+IYPnYPF4q8BIwS1U/iVTH6Kox\n6gwjLpSVlXHgwIFoOZI3zTCMhmM6tkr+AOSr6qOBuiKgN1Dkz90VtyCyYSSE2jRqixcv5tZbb2XP\nnj1kZGQwfPhw3njjDcDJA/bv38/hw4dZsmQJy5cvZ9CgQcl0wzCSiunYnF2/wk1at1dregWY5suT\ngFXV368ZRjwZO3YsR48eRUQ4/vjjycrKYsGCBdxzzz3s3LmTNWvWsHv37uikNnPmTNq1a0ePHj1Y\nuHAhRUVFNqkZrZ5E69iuB14Vkd/h7pr248L4Y+nYxgD9/eHNpmMTkf64lUUOA2Uish/IUtW5uPd5\nV4rINTgdXkai7DCMCA3VsH300UfMmzePLVu2sHPnTi6++GI+/vhjy3RttHoSHe7/BPBAJDLSh/t3\nqUXHVgQUQLPnY9sJjA2G+wPFvu194F+0Mm3NrcA7dZzLMOJObRq2pUuXMmXKFNq3b0/fvn3p378/\n7733HmeffXYzW2gYLYtWFe4fy0hVPQjEDPfXBqatCWI6ttQmWf41RcNWXFzM6NGjo/tpaWkUFxfX\n2t8wWgutKtwfp0WLpWP7EGKG+we5FlgWyyDTsYWHZPnXFA1bUVERW7dujR67a9euOnOthV0HZf4Z\nEVpV2pq6dGy1hPtH2upMW2M6tvDQkvyrT8O2du1agGj7zJkzueSSS2p9FBl2HZT5Z0RIZFTkFtwa\ni9VJWLg/VSfqeIT7B9PWZFraGiORNFbDlpmZybx58zh06BA7duwgPz+fUaOSkgjDMFoUFu5P7eH+\nlrbGiFBRUcGIESOYONHFIc2ePZv+/fsjIuzdW7kQzdKlSxk6dGg05cw77zQ81mj37t2MGTOGYcOG\nMWrUKDIyMqIatrS0NNauXUtGRgaXXnopAIMHD2by5MkMGjSIcePGMWfOHIuINAwsbQ0ikoa7I9sG\nfODT08z24f6WtsYA4LHHHuOss85i//79AJx77rlMnDixxqOhiy66iMzMTESETZs2MXnyZLZt29ag\na/Tr14+8vLwa9VdccUVUtF2drKwssrKyGueMYYScVp+2BreSyOu46Mlv4dLWzPVtL+AmtwivYLQ6\nioqK+Mtf/kJWVhaPPPIIACNGjIjZt3Pn6K8nZWVl+H+IDMNoRhL2KDKQtiZHVU9X1UG40Pyeibrm\nMfCwqg7ERVGeKyLjA23zVXW43+bWcrwRYm6//XYefPBB2rRp2J/L4sWLGThwIBkZGTz99NMJts4w\njOq0Kh1bE9LWNBrTsaU2Qf8Kf53Ba6+9ximnnMLIkSMbHGodeXT41ltvce+997JixYoEWmwYRnVa\nlY6tCWlrAH4gIufjkqPeoaqfxTguqmPr0aMHfx7XqRHupBalpaVktxL/cnJyeOmll1i+fDmLFi3i\n8OHDHDx4kO9973vR91rffPMNa9asoWvXrjHPt2XLFpYuXVpre3MSdh2U+WdEUdWEbMB04Lcx6n8L\n/Diw/zzu7qoPsNnXXY0L4Ij0eQ240JcP4d7TAczAresI7rHqvsDxfwwcvwwYU4+97Xy/2wN1JwPt\nffm/cIsg1+n3gAEDNMysXr062SYklLr8W716tWZkZFSpS09P1z179kT38/Pz9ejRo6qqmpubq6ee\nemp0P9m05rELA2H3D1ivcZp/TMdWSQ0dm6qWaKV27Y/E9sdohcyaNYu0tDSKiooYOnQo1113HQAL\nFy5kyJAhDB8+nJtvvpn58+dbAIlhNDOmY6NOHVuvwG4msDWRdhgtl4qKCu64447ofps2bejQoQMV\nFRVs2rSJuXNdXFFaWhpt27aNpp7p0qVLskw2jFaL6djq1rFNF5FM3B3kF7hHnEYrpKE6tr59+/Lm\nm29y4oknsmzZMq6//nrWrVuXBIsNo/XS6nVsqlokIg8AVwG9g+cH/untV5zGLZF3uEYLpTE6tnPO\nOSdaHj16NEVFRc1io2EYlSQ6H9ti4FlVneLrhuN0bB8n6rpN5FVgNpBfrf5PWplLLhN4BBhX14ks\n3D+1qR7uD5U6tsg6jg3lqaeeYvz48fV3NAwjrpiOzenY3vXXqdKgqvsDu528vTWwtDXhIehfTk4O\na9eupby8nAMHDrBx40ZKSkqqhFzXFu6/YcMGHn/8cWbNmtViQrTDHi5u/hlR4hVeWX2j9nD/HwB/\nA9ri7t4+BXrR8HB/Bcb78mJgOe4x4TBgY+D4T3ABIR1wjxR7N8Dm0hh1NwP/AD4DzqjvHBbun9pU\n9++uu+7S0047TdPT07Vnz556/PHH69SpU6Pt1cP9VVXz8vK0X79+un379uYwucG0trELG2H3jxQJ\n96+NaD42Vd0NRPKxNZTq+djeVNVyX+4T6LdSVb9S1W+Aj4D0phirqnNU9XTgTuCeppzDSF1mzpxJ\nUVERhYWFzJs3j7Fjx/LCCy/U2v/TTz/l+9//Ps8//zwDBgxoRksNw4hgOraGMw+4/BjPYYSE2nRs\nM2bMoKSkhJtuuimausYwjOYlke/YVgEPiMhPVPWPUEPH9ixwEk7H9lOqTl6FwE0i0gY4jQTr2GpD\nRM5Q1UhASQY1g0uMFOObb77h/PPP59ChQxw5coRJkyZx//33c95553HgwAFKS0s5ePAgo0aNYsmS\nJagqt912G6+//jodO3YkOzsbgOnTpzN9+vQa5587d25U02YYRnJo9To2ABF5ELgS6CgiRcBcdVKC\nW3wG7XLchDwtkXYYiad9+/asWrWKzp07U15ezpgxYxg/fjxvv/024AJGHn/8cS677DIAli1bRn5+\nPvn5+axbt44bb7zRdGmG0cJp9To2EekIDAZKcSuLvBo5r6reJiKTgV8Ap+CE3FfWdi6j5SMi0Zxp\n5eXllJeXV4mGPXjwIKtWreKZZ54BXEbsq666ChFh9OjR7Nu3j127dtGrV6+Y5zcMI/mYjs3xsKqu\nFpHjgJUiMl5Vl4nIGcDdwLmq+qWInFLfiUzH1nKJ6NIqKioYOXIkBQUF3HzzzXz3u5VJH95++20u\nuugiTjjhBACKi4vp3bt3tD0tLY3i4mKb2AyjBWM6trrzsf0EmKOqX/r2z2M5ajq21CCoAXr00Ucp\nLS3l3nvvZeDAgfTt2xeA5cuXk5mZGe27d+9eNmzYwJEjzucvv/yS3NxcSktLm9v8YybsOijzz4hg\n+dgCxMjHNsDXr8Hp7n6hqn+tfpyq/gGXHYAzzzxTb516WSPcSS1ycnKYXG19xFQmNzeXkpISrrnm\nGkpKSigoKODOO++kQwcXyzRs2DC6d+8eXROyrKyMzMzMlLxjy8nJqbG2ZZgw/4wIpmPziEg74CVg\nlqp+4qvbAWcAFwI/Aub6yc9IUfbs2cO+ffsA+Prrr1mxYgUDBw4EYMGCBYwePTo6qQFkZmby3HPP\noaq8++67dO3aNSUnNcNoTSTyjm0LMClGfcJ0bH5yinDM+diAIuBdP3HuEJHtuInu/Qb4YLRAdu3a\nxbRp06ioqODo0aNMnjyZiRNdbNG8efOYMGFClf4TJkzg9ddfp3///nTs2DEaVGIYRsvFdGxUycd2\nXbWmJbg7tWwR6Y57NPkJRsoydOhQNmzYELMtJyenxjsMEWHOnDnNYJlhGPGi1evY6snH9gZwiYh8\nhLvr+6mqliTKFsMwDOPYSaiOTVV3ApNjNNXQsalqIS7gpNnzsVHL41Fvx3/7zTAMw0gBLHGmYRiG\nESqkMg4j/NShY/swjtc4AGyP1/laIN2Bvck2IoGE2b8w+wbmX6pzpqp2iceJEr2kVouiPh1bnNiu\nqqFd0l1E1pt/qUmYfQPzL9URkfXxOpc9ijQMwzBChU1shmEYRqiwiS3+/CHZBiQY8y91CbNvYP6l\nOnHzr1UFjxiGYRjhx+7YDMMwjFBhE5thGIYRKmxiiyMiMk5EtotIgV9GLKUQkd4islpEtorIFhG5\nzdefJCJ/E5F8/3mirxcRmeX93SQi306uBw1DRNqKyAaf5w8R6Ssi67x/833CWUSkvd8v8O19kml3\nQxCRbiLysohs8+N4dljGT0Tu8L+Xm0XkJRHpkMpjJyJPi8jnIrI5UNfosRKRab5/vohMS4YvsajF\nv4f87+YmEVkczJYiInd7/7aLyKWB+sZ/r6qqbXHYcPna/gH0A47DrYU5KNl2NdKHXsC3fbkLLtP5\nIFwi17t8/V3Ab3x5ArAMtyTZaGBdsn1ooJ//DfwJeM3v/xmY4stPADf68k3AE748BZifbNsb4Nuz\nwHW+fBzQLQzjh1sMfQdwfGDMrk7lscMtAP9tYHOgrlFjhVtI/hP/eaIvn5hs3+rw7xKgnS//JuDf\nIP+d2R7o679L2zb1ezXpzodlA84G3gjs3w3cnWy7jtGnpbgkrtuBXr6uF06EDvAk8KNA/2i/lrrh\nsqOvBMYCr/kvir2BP7boOOIWwT7bl9v5fpJsH+rw7QT/5S/V6lN+/PzE9pn/Am/nx+7SVB87XA7J\n4Bd/o8YKl33kyUB9lX7J3qr7V63tCuBFX67yfRkZv6Z+r9qjyPgR+cOLUOTrUhL/6GYEsA7oqaq7\nAPznKb5bKvr8KPAzXC4/gJOBfap6xO8HfYj659u/8v1bKv2APcAz/lHrXBHpRAjGT1WLgYeBT4Fd\nuLHIJTxjF6GxY5UyYxiDH+PuQiHO/tnEFj9iZQhISS2FiHQGFgK3q+r+urrGqGuxPovIROBzVc0N\nVsfoqg1oa4m0wz36+b2qjgDKcI+zaiNl/PPvmi7DPaY6FegEjI/RNVXHrj5q8ycl/RSRLFxC6Rcj\nVTG6Ndk/m9jiRxHQO7CfBuxMki1NRkS+hZvUXlTVRb56t4j08u29gM99far5fC6QKSKFwDzc48hH\ngW5SmX096EPUP9/eFfiiOQ1uJEVAkaqu8/sv4ya6MIzfxcAOVd2jLqP9IuAcwjN2ERo7Vqk0hoAL\ndgEmAlPVP18kzv7ZxBY/3gfO8FFax+FeWL+SZJsahYgI8BS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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a96a21cda0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 8 log loss 0.26213798042870184 in 2052\n",
      "Fold :  9\n",
      "Training until validation scores don't improve for 1000 rounds.\n",
      "[100]\ttraining's multi_logloss: 0.533528\tvalid_1's multi_logloss: 0.524432\n",
      "[200]\ttraining's multi_logloss: 0.366241\tvalid_1's multi_logloss: 0.354908\n",
      "[300]\ttraining's multi_logloss: 0.306492\tvalid_1's multi_logloss: 0.295015\n",
      "[400]\ttraining's multi_logloss: 0.282572\tvalid_1's multi_logloss: 0.271461\n",
      "[500]\ttraining's multi_logloss: 0.272067\tvalid_1's multi_logloss: 0.261301\n",
      "[600]\ttraining's multi_logloss: 0.266987\tvalid_1's multi_logloss: 0.256631\n",
      "[700]\ttraining's multi_logloss: 0.264299\tvalid_1's multi_logloss: 0.25433\n",
      "[800]\ttraining's multi_logloss: 0.262733\tvalid_1's multi_logloss: 0.253106\n",
      "[900]\ttraining's multi_logloss: 0.261748\tvalid_1's multi_logloss: 0.252413\n",
      "[1000]\ttraining's multi_logloss: 0.261101\tvalid_1's multi_logloss: 0.252017\n",
      "[1100]\ttraining's multi_logloss: 0.260661\tvalid_1's multi_logloss: 0.251786\n",
      "[1200]\ttraining's multi_logloss: 0.260332\tvalid_1's multi_logloss: 0.251648\n",
      "[1300]\ttraining's multi_logloss: 0.260067\tvalid_1's multi_logloss: 0.251575\n",
      "[1400]\ttraining's multi_logloss: 0.259847\tvalid_1's multi_logloss: 0.251533\n",
      "[1500]\ttraining's multi_logloss: 0.259656\tvalid_1's multi_logloss: 0.251513\n",
      "[1600]\ttraining's multi_logloss: 0.259483\tvalid_1's multi_logloss: 0.251515\n",
      "[1700]\ttraining's multi_logloss: 0.259314\tvalid_1's multi_logloss: 0.251524\n",
      "[1800]\ttraining's multi_logloss: 0.259149\tvalid_1's multi_logloss: 0.25155\n",
      "[1900]\ttraining's multi_logloss: 0.258992\tvalid_1's multi_logloss: 0.251592\n",
      "[2000]\ttraining's multi_logloss: 0.258838\tvalid_1's multi_logloss: 0.251616\n",
      "[2100]\ttraining's multi_logloss: 0.258687\tvalid_1's multi_logloss: 0.251647\n",
      "[2200]\ttraining's multi_logloss: 0.258536\tvalid_1's multi_logloss: 0.251667\n",
      "[2300]\ttraining's multi_logloss: 0.258388\tvalid_1's multi_logloss: 0.251694\n",
      "[2400]\ttraining's multi_logloss: 0.258245\tvalid_1's multi_logloss: 0.251711\n",
      "[2500]\ttraining's multi_logloss: 0.258105\tvalid_1's multi_logloss: 0.251736\n",
      "Early stopping, best iteration is:\n",
      "[1525]\ttraining's multi_logloss: 0.259613\tvalid_1's multi_logloss: 0.251509\n"
     ]
    },
    {
     "data": {
      "image/png": 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YhpGDnTt3+rL9srKyuO6667jyyisBmDJlCgMGDMjVp0GDBhw4cIDjx4/zySef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\nrsNdfYXb1uK0omBxWkfbuHEje/bsIT09nb59+7J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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1a9668827f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 9 log loss 0.2515086020611269 in 1525\n",
      "Training  Finish\n"
     ]
    }
   ],
   "source": [
    "pred, log_loss, oofs = fit_every_feature_model(ensemble_trains, labels, ensemble_tests, fold_count=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2619215273808303"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "log_loss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 0, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "oofs = np.concatenate(oofs)\n",
    "oofs.argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def np_weighted_accuracy(y_true, y_pred):\n",
    "    weight = np.array([[1/16, 1/15, 1/5]])\n",
    "    norm = [(1/16) + (1/15) + (1/5)]\n",
    "    weight_mask = weight * y_true\n",
    "    weight_mask = np.max(weight_mask, axis=-1)\n",
    "    norms = np.sum(weight_mask)\n",
    "    \n",
    "    y_true = np.argmax(y_true, axis=-1)\n",
    "    y_pred = np.argmax(y_pred, axis=-1)\n",
    "    \n",
    "    res = ((y_true == y_pred) * weight_mask).sum() / norms\n",
    "    return res"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np_weighted_accuracy' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-1-a174e25a5248>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mscore\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp_weighted_accuracy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mto_categorical\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabels\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moofs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'np_weighted_accuracy' is not defined"
     ]
    }
   ],
   "source": [
    "score = np_weighted_accuracy(to_categorical(labels), oofs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "score 0.8601171890929914\n",
      "Predicting training results...\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "unhashable type: 'slice'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-53-76fc89728cc3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     11\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     12\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Predicting training results...\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 13\u001b[1;33m \u001b[0moofs\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m\"unrelated\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0moofs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"agreed\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0moofs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"disagreed\"\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0moofs\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     14\u001b[0m \u001b[0msubmit_path\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0moofs_path\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;34m\"-Train-L{:4f}-NB{:d}.csv\"\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mscore\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mNB_WORDS\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m \u001b[0moofs\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mto_csv\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubmit_path\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1962\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1963\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1964\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1965\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1966\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1969\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1970\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1971\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1972\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1973\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   1641\u001b[0m         \u001b[1;34m\"\"\"Return the cached item, item represents a label indexer.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1642\u001b[0m         \u001b[0mcache\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_item_cache\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1643\u001b[1;33m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1644\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1645\u001b[0m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: unhashable type: 'slice'"
     ]
    }
   ],
   "source": [
    "print(\"score\", score)\n",
    "oofs_dir = \"../data/ensemble/oofs/\"\n",
    "output_dir = \"../data/ensemble/pred/\"\n",
    "onehot_pred_dir = \"../data/ensemble/nn_one_hot/\"\n",
    "\n",
    "model_submit_prefix = \"LightGBM-Ensemble\"\n",
    "\n",
    "oofs_path = oofs_dir + model_submit_prefix\n",
    "output_path = output_dir + model_submit_prefix\n",
    "one_hot_pred_path = onehot_pred_dir + \"One-Hot\" + model_submit_prefix\n",
    "\n",
    "print(\"Predicting training results...\")\n",
    "oofs = pd.DataFrame({\"unrelated\": oofs[:, 0], \"agreed\": oofs[:, 1], \"disagreed\": oofs[:, 2]})\n",
    "submit_path = oofs_path + \"-Train-L{:4f}-NB{:d}.csv\".format(score, NB_WORDS)\n",
    "oofs.to_csv(submit_path, index=False)\n",
    "\n",
    "test_predicts = pd.DataFrame({\"unrelated\": pred[:, 0], \"agreed\": pred[:, 1], \"disagreed\": pred[:, 2]})\n",
    "submit_path = output_path + \"-L{:4f}-NB{:d}.csv\".format(score, NB_WORDS)\n",
    "test_predicts.to_csv(submit_path, index=False) # 0.3343\n",
    "\n",
    "print(\"Predicting labeled testing results...\")\n",
    "ids = pd.read_csv(\"../data/dataset/test.csv\")\n",
    "pred_labels = test_predicts.idxmax(axis=1)\n",
    "sub = pd.DataFrame({\"Id\": ids['id'].values, \"Category\": pred_labels})\n",
    "submit_path = one_hot_pred_path + \"-L{:4f}-NB{:d}.csv\".format(score, NB_WORDS)\n",
    "sub.to_csv(submit_path, index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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